Carrel name: keyword-cxr-cord Creating study carrel named keyword-cxr-cord Initializing database file: cache/cord-103840-2diao7zh.json key: cord-103840-2diao7zh authors: Mungai, B. N.; Joekes, E.; Masini, E.; Obasi, A.; Manduku, V.; Mugi, B.; Ongango, J.; Kirathe, D.; Kiplimo, R.; Sitienei, J.; Oronje, R.; Morton, B.; Squire, S. B.; MacPherson, P. title: It's not TB but what could it be? Abnormalities on chest X-rays taken during the Kenya National Tuberculosis Prevalence Survey date: 2020-08-22 journal: nan DOI: 10.1101/2020.08.19.20177907 sha: doc_id: 103840 cord_uid: 2diao7zh file: cache/cord-157444-huvnyali.json key: cord-157444-huvnyali authors: Nabulsi, Zaid; Sellergren, Andrew; Jamshy, Shahar; Lau, Charles; Santos, Eddie; Kiraly, Atilla P.; Ye, Wenxing; Yang, Jie; Kazemzadeh, Sahar; Yu, Jin; Kalidindi, Raju; Etemadi, Mozziyar; Vicente, Florencia Garcia; Melnick, David; Corrado, Greg S.; Peng, Lily; Eswaran, Krish; Tse, Daniel; Beladia, Neeral; Liu, Yun; Chen, Po-Hsuan Cameron; Shetty, Shravya title: Deep Learning for Distinguishing Normal versus Abnormal Chest Radiographs and Generalization to Unseen Diseases date: 2020-10-22 journal: nan DOI: nan sha: doc_id: 157444 cord_uid: huvnyali file: cache/cord-292341-uo54ghf3.json key: cord-292341-uo54ghf3 authors: Cocconcelli, Elisabetta; Biondini, Davide; Giraudo, Chiara; Lococo, Sara; Bernardinello, Nicol; Fichera, Giulia; Barbiero, Giulio; Castelli, Gioele; Cavinato, Silvia; Ferrari, Anna; Saetta, Marina; Cattelan, Annamaria; Spagnolo, Paolo; Balestro, Elisabetta title: Clinical Features and Chest Imaging as Predictors of Intensity of Care in Patients with COVID-19 date: 2020-09-16 journal: J Clin Med DOI: 10.3390/jcm9092990 sha: doc_id: 292341 cord_uid: uo54ghf3 file: cache/cord-006708-nionk55w.json key: cord-006708-nionk55w authors: Aktaş, Fatma; Aktaş, Turan title: The pulmonary findings of Crimean–Congo hemorrhagic fever patients with chest X-ray assessments date: 2019-03-25 journal: Radiol Med DOI: 10.1007/s11547-019-01024-w sha: doc_id: 6708 cord_uid: nionk55w file: cache/cord-238881-tupom7fb.json key: cord-238881-tupom7fb authors: Yeh, Chun-Fu; Cheng, Hsien-Tzu; Wei, Andy; Chen, Hsin-Ming; Kuo, Po-Chen; Liu, Keng-Chi; Ko, Mong-Chi; Chen, Ray-Jade; Lee, Po-Chang; Chuang, Jen-Hsiang; Chen, Chi-Mai; Chen, Yi-Chang; Lee, Wen-Jeng; Chien, Ning; Chen, Jo-Yu; Huang, Yu-Sen; Chang, Yu-Chien; Huang, Yu-Cheng; Chou, Nai-Kuan; Chao, Kuan-Hua; Tu, Yi-Chin; Chang, Yeun-Chung; Liu, Tyng-Luh title: A Cascaded Learning Strategy for Robust COVID-19 Pneumonia Chest X-Ray Screening date: 2020-04-24 journal: nan DOI: nan sha: doc_id: 238881 cord_uid: tupom7fb file: cache/cord-331891-a6b1xanm.json key: cord-331891-a6b1xanm authors: Cozzi, Diletta; Albanesi, Marco; Cavigli, Edoardo; Moroni, Chiara; Bindi, Alessandra; Luvarà, Silvia; Lucarini, Silvia; Busoni, Simone; Mazzoni, Lorenzo Nicola; Miele, Vittorio title: Chest X-ray in new Coronavirus Disease 2019 (COVID-19) infection: findings and correlation with clinical outcome date: 2020-06-09 journal: Radiol Med DOI: 10.1007/s11547-020-01232-9 sha: doc_id: 331891 cord_uid: a6b1xanm file: cache/cord-266672-t85wd0xq.json key: cord-266672-t85wd0xq authors: Bagnera, Silvia; Bisanti, Francesca; Tibaldi, Claudia; Pasquino, Massimo; Berrino, Giulia; Ferraro, Roberta; Patania, Sebastiano title: Performance of Radiologists in the Evaluation of the Chest Radiography with the Use of a “new software score” in Coronavirus Disease 2019 Pneumonia Suspected Patients date: 2020-07-20 journal: J Clin Imaging Sci DOI: 10.25259/jcis_76_2020 sha: doc_id: 266672 cord_uid: t85wd0xq file: cache/cord-127759-wpqdtdjs.json key: cord-127759-wpqdtdjs authors: Qi, Xiao; Brown, Lloyd; Foran, David J.; Hacihaliloglu, Ilker title: Chest X-ray Image Phase Features for Improved Diagnosis of COVID-19 Using Convolutional Neural Network date: 2020-11-06 journal: nan DOI: nan sha: doc_id: 127759 cord_uid: wpqdtdjs file: cache/cord-337507-cqbbrnku.json key: cord-337507-cqbbrnku authors: Cozzi, Andrea; Schiaffino, Simone; Arpaia, Francesco; Pepa, Gianmarco Della; Tritella, Stefania; Bertolotti, Pietro; Menicagli, Laura; Monaco, Cristian Giuseppe; Carbonaro, Luca Alessandro; Spairani, Riccardo; Paskeh, Bijan Babaei; Sardanelli, Francesco title: Chest x-ray in the COVID-19 pandemic: Radiologists’ real-world reader performance date: 2020-09-10 journal: Eur J Radiol DOI: 10.1016/j.ejrad.2020.109272 sha: doc_id: 337507 cord_uid: cqbbrnku file: cache/cord-275974-uqd30v7b.json key: cord-275974-uqd30v7b authors: Shorfuzzaman, Mohammad; Hossain, M. Shamim title: MetaCOVID: A Siamese neural network framework with contrastive loss for n-shot diagnosis of COVID-19 patients date: 2020-10-17 journal: Pattern Recognit DOI: 10.1016/j.patcog.2020.107700 sha: doc_id: 275974 cord_uid: uqd30v7b file: cache/cord-018027-goxdiyv3.json key: cord-018027-goxdiyv3 authors: Heussel, Claus Peter title: Diagnostic Radiology in Hematological Patients with Febrile Neutropenia date: 2014-11-27 journal: Infections in Hematology DOI: 10.1007/978-3-662-44000-1_7 sha: doc_id: 18027 cord_uid: goxdiyv3 file: cache/cord-296208-uy1r6lt2.json key: cord-296208-uy1r6lt2 authors: Greenspan, Hayit; San José Estépar, Raúl; J. Niessen, Wiro; Siegel, Eliot; Nielsen, Mads title: Position paper on COVID-19 imaging and AI: from the clinical needs and technological challenges to initial AI solutions at the lab and national level towards a new era for AI in healthcare date: 2020-08-19 journal: Med Image Anal DOI: 10.1016/j.media.2020.101800 sha: doc_id: 296208 cord_uid: uy1r6lt2 file: cache/cord-028786-400vglzm.json key: cord-028786-400vglzm authors: Oloko-Oba, Mustapha; Viriri, Serestina title: Diagnosing Tuberculosis Using Deep Convolutional Neural Network date: 2020-06-05 journal: Image and Signal Processing DOI: 10.1007/978-3-030-51935-3_16 sha: doc_id: 28786 cord_uid: 400vglzm file: cache/cord-303483-wendrxee.json key: cord-303483-wendrxee authors: Rubin, Geoffrey D.; Ryerson, Christopher J.; Haramati, Linda B.; Sverzellati, Nicola; Kanne, Jeffrey P.; Raoof, Suhail; Schluger, Neil W.; Volpi, Annalisa; Yim, Jae-Joon; Martin, Ian B. K.; Anderson, Deverick J.; Kong, Christina; Altes, Talissa; Bush, Andrew; Desai, Sujal R.; Goldin, Jonathan; Goo, Jin Mo; Humbert, Marc; Inoue, Yoshikazu; Kauczor, Hans-Ulrich; Luo, Fengming; Mazzone, Peter J.; Prokop, Mathias; Remy-Jardin, Martine; Richeldi, Luca; Schaefer-Prokop, Cornelia M.; Tomiyama, Noriyuki; Wells, Athol U.; Leung, Ann N. title: The Role of Chest Imaging in Patient Management during the COVID-19 Pandemic: A Multinational Consensus Statement from the Fleischner Society date: 2020-04-07 journal: Radiology DOI: 10.1148/radiol.2020201365 sha: doc_id: 303483 cord_uid: wendrxee file: cache/cord-282198-ugmv9om1.json key: cord-282198-ugmv9om1 authors: Pare, Joseph R.; Camelo, Ingrid; Mayo, Kelly C.; Leo, Megan M.; Dugas, Julianne N.; Nelson, Kerrie P.; Baker, William E.; Shareef, Faizah; Mitchell, Patricia M.; Schechter-Perkins, Elissa M. title: Point-of-care Lung Ultrasound Is More Sensitive than Chest Radiograph for Evaluation of COVID-19 date: 2020-06-19 journal: West J Emerg Med DOI: 10.5811/westjem.2020.5.47743 sha: doc_id: 282198 cord_uid: ugmv9om1 file: cache/cord-013065-oj0wsstz.json key: cord-013065-oj0wsstz authors: Rodríguez-Fanjul, Javier; Guitart, Carmina; Bobillo-Perez, Sara; Balaguer, Mònica; Jordan, Iolanda title: Procalcitonin and lung ultrasound algorithm to diagnose severe pneumonia in critical paediatric patients (PROLUSP study). A randomised clinical trial date: 2020-10-08 journal: Respir Res DOI: 10.1186/s12931-020-01476-z sha: doc_id: 13065 cord_uid: oj0wsstz file: cache/cord-269014-ck27fm58.json key: cord-269014-ck27fm58 authors: Vo, Luan Nguyen Quang; Codlin, Andrew James; Huynh, Huy Ba; Mai, Thuy Doan To; Forse, Rachel Jeanette; Truong, Vinh Van; Dang, Ha Minh Thi; Nguyen, Bang Duc; Nguyen, Lan Huu; Nguyen, Tuan Dinh; Nguyen, Hoa Binh; Nguyen, Nhung Viet; Caws, Maxine; Lonnroth, Knut; Creswell, Jacob title: Enhanced Private Sector Engagement for Tuberculosis Diagnosis and Reporting through an Intermediary Agency in Ho Chi Minh City, Viet Nam date: 2020-09-14 journal: Trop Med Infect Dis DOI: 10.3390/tropicalmed5030143 sha: doc_id: 269014 cord_uid: ck27fm58 file: cache/cord-312251-t6omrr07.json key: cord-312251-t6omrr07 authors: Vancheri, Sergio Giuseppe; Savietto, Giovanni; Ballati, Francesco; Maggi, Alessia; Canino, Costanza; Bortolotto, Chandra; Valentini, Adele; Dore, Roberto; Stella, Giulia Maria; Corsico, Angelo Guido; Iotti, Giorgio Antonio; Mojoli, Francesco; Perlini, Stefano; Bruno, Raffaele; Preda, Lorenzo title: Radiographic findings in 240 patients with COVID-19 pneumonia: time-dependence after the onset of symptoms date: 2020-05-30 journal: Eur Radiol DOI: 10.1007/s00330-020-06967-7 sha: doc_id: 312251 cord_uid: t6omrr07 file: cache/cord-334495-7y1la856.json key: cord-334495-7y1la856 authors: Agricola, Eustachio; Beneduce, Alessandro; Esposito, Antonio; Ingallina, Giacomo; Palumbo, Diego; Palmisano, Anna; Ancona, Francesco; Baldetti, Luca; Pagnesi, Matteo; Melisurgo, Giulio; Zangrillo, Alberto; De Cobelli, Francesco title: Heart and Lung Multimodality Imaging in COVID-19 date: 2020-06-24 journal: JACC Cardiovasc Imaging DOI: 10.1016/j.jcmg.2020.05.017 sha: doc_id: 334495 cord_uid: 7y1la856 file: cache/cord-350636-ufwfitue.json key: cord-350636-ufwfitue authors: Shumilov, Evgenii; Hosseini, Ali Seif Amir; Petzold, Golo; Treiber, Hannes; Lotz, Joachim; Ellenrieder, Volker; Kunsch, Steffen; Neesse, Albrecht title: Comparison of Chest Ultrasound and Standard X-Ray Imaging in COVID-19 Patients date: 2020-09-02 journal: Ultrasound Int Open DOI: 10.1055/a-1217-1603 sha: doc_id: 350636 cord_uid: ufwfitue file: cache/cord-294557-4h0sybiy.json key: cord-294557-4h0sybiy authors: Stogiannos, N.; Fotopoulos, D.; Woznitza, N.; Malamateniou, C. title: Coronavirus disease 2019 (COVID-19) in the radiology department: What radiographers need to know date: 2020-06-04 journal: Radiography (Lond) DOI: 10.1016/j.radi.2020.05.012 sha: doc_id: 294557 cord_uid: 4h0sybiy file: cache/cord-297198-dneycnyr.json key: cord-297198-dneycnyr authors: Khan, T.; Lopez, T.; Khan, T.; Ali, A.; Syed, S.; Patil, P.; Hatoum, A. title: Re: a British Society of Thoracic Imaging statement: considerations in designing local imaging diagnostic algorithms for the COVID-19 pandemic date: 2020-05-27 journal: Clin Radiol DOI: 10.1016/j.crad.2020.05.009 sha: doc_id: 297198 cord_uid: dneycnyr file: cache/cord-345528-rk16pt0i.json key: cord-345528-rk16pt0i authors: Yasar, Y.; Karli, B. T.; Coteli, C.; Coteli, M. B. title: MantisCOVID: Rapid X-Ray Chest Radiograph and Mortality Rate Evaluation With Artificial Intelligence For COVID-19 date: 2020-05-08 journal: nan DOI: 10.1101/2020.05.04.20090779 sha: doc_id: 345528 cord_uid: rk16pt0i file: cache/cord-006683-7rsmbk3j.json key: cord-006683-7rsmbk3j authors: Coppola, M.; Porto, A.; De Santo, D.; De Fronzo, S.; Grassi, R.; Rotondo, A. title: Influenza A virus: radiological and clinical findings of patients hospitalised for pandemic H1N1 influenza date: 2011-01-12 journal: Radiol Med DOI: 10.1007/s11547-011-0622-0 sha: doc_id: 6683 cord_uid: 7rsmbk3j file: cache/cord-297396-r1p7xn3a.json key: cord-297396-r1p7xn3a authors: Ng, Ming-Yen; Wan, Eric Yuk Fai; Wong, Ho Yuen Frank; Leung, Siu Ting; Lee, Jonan Chun Yin; Chin, Thomas Wing-Yan; Lo, Christine Shing Yen; Lui, Macy Mei-Sze; Chan, Edward Hung Tat; Fong, Ambrose Ho-Tung; Yung, Fung Sau; Ching, On Hang; Chiu, Keith Wan-Hang; Chung, Tom Wai Hin; Vardhanbhuti, Varut; Lam, Hiu Yin Sonia; To, Kelvin Kai Wang; Chiu, Jeffrey Long Fung; Lam, Tina Poy Wing; Khong, Pek Lan; Liu, Raymond Wai To; Man Chan, Johnny Wai; Ka Lun Alan, Wu; Lung, Kwok-Cheung; Hung, Ivan Fan Ngai; Lau, Chak Sing; Kuo, Michael D.; Ip, Mary Sau-Man title: Development and Validation of Risk Prediction Models for COVID-19 Positivity in a Hospital Setting date: 2020-09-15 journal: Int J Infect Dis DOI: 10.1016/j.ijid.2020.09.022 sha: doc_id: 297396 cord_uid: r1p7xn3a file: cache/cord-327257-doygrgrc.json key: cord-327257-doygrgrc authors: Zhu, Jocelyn; Shen, Beiyi; Abbasi, Almas; Hoshmand-Kochi, Mahsa; Li, Haifang; Duong, Tim Q. title: Deep transfer learning artificial intelligence accurately stages COVID-19 lung disease severity on portable chest radiographs date: 2020-07-28 journal: PLoS One DOI: 10.1371/journal.pone.0236621 sha: doc_id: 327257 cord_uid: doygrgrc file: cache/cord-167889-um3djluz.json key: cord-167889-um3djluz authors: Chen, Jianguo; Li, Kenli; Zhang, Zhaolei; Li, Keqin; Yu, Philip S. title: A Survey on Applications of Artificial Intelligence in Fighting Against COVID-19 date: 2020-07-04 journal: nan DOI: nan sha: doc_id: 167889 cord_uid: um3djluz file: cache/cord-336843-c0sr3six.json key: cord-336843-c0sr3six authors: Gerritsen, M. G.; Willemink, M. J.; Pompe, E.; van der Bruggen, T.; van Rhenen, A.; Lammers, J. W. J.; Wessels, F.; Sprengers, R. W.; de Jong, P. A.; Minnema, M. C. title: Improving early diagnosis of pulmonary infections in patients with febrile neutropenia using low-dose chest computed tomography date: 2017-02-24 journal: PLoS One DOI: 10.1371/journal.pone.0172256 sha: doc_id: 336843 cord_uid: c0sr3six file: cache/cord-347691-ia2i8svg.json key: cord-347691-ia2i8svg authors: Larici, Anna Rita; Cicchetti, Giuseppe; Marano, Riccardo; Merlino, Biagio; Elia, Lorenzo; Calandriello, Lucio; del Ciello, Annemilia; Farchione, Alessandra; Savino, Giancarlo; Infante, Amato; Larosa, Luigi; Colosimo, Cesare; Manfredi, Riccardo; Natale, Luigi title: Multimodality imaging of COVID-19 pneumonia: from diagnosis to follow-up. A comprehensive review date: 2020-08-17 journal: Eur J Radiol DOI: 10.1016/j.ejrad.2020.109217 sha: doc_id: 347691 cord_uid: ia2i8svg file: cache/cord-310228-bqpvykce.json key: cord-310228-bqpvykce authors: Borkowski, A. A.; Viswanadham, N. A.; Thomas, L. B.; Guzman, R. D.; Deland, L. A.; Mastorides, S. M. title: Using Artificial Intelligence for COVID-19 Chest X-ray Diagnosis date: 2020-05-26 journal: nan DOI: 10.1101/2020.05.21.20106518 sha: doc_id: 310228 cord_uid: bqpvykce file: cache/cord-355218-eici4eit.json key: cord-355218-eici4eit authors: Punn, Narinder Singh; Agarwal, Sonali title: Automated diagnosis of COVID-19 with limited posteroanterior chest X-ray images using fine-tuned deep neural networks date: 2020-10-17 journal: Appl Intell DOI: 10.1007/s10489-020-01900-3 sha: doc_id: 355218 cord_uid: eici4eit file: cache/cord-346942-88l03lf0.json key: cord-346942-88l03lf0 authors: Kerpel, Ariel; Apter, Sara; Nissan, Noam; Houri-Levi, Esther; Klug, Maximiliano; Amit, Sharon; Konen, Eli; Marom, Edith M. title: Diagnostic and Prognostic Value of Chest Radiographs for COVID-19 at Presentation date: 2020-08-17 journal: West J Emerg Med DOI: 10.5811/westjem.2020.7.48842 sha: doc_id: 346942 cord_uid: 88l03lf0 file: cache/cord-015352-2d02eq3y.json key: cord-015352-2d02eq3y authors: nan title: ESPR 2017 date: 2017-04-26 journal: Pediatr Radiol DOI: 10.1007/s00247-017-3820-2 sha: doc_id: 15352 cord_uid: 2d02eq3y Reading metadata file and updating bibliogrpahics === updating bibliographic database Building study carrel named keyword-cxr-cord === file2bib.sh === id: cord-238881-tupom7fb author: Yeh, Chun-Fu title: A Cascaded Learning Strategy for Robust COVID-19 Pneumonia Chest X-Ray Screening date: 2020-04-24 pages: extension: .txt txt: ./txt/cord-238881-tupom7fb.txt cache: ./cache/cord-238881-tupom7fb.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-238881-tupom7fb.txt' === file2bib.sh === id: cord-297198-dneycnyr author: Khan, T. title: Re: a British Society of Thoracic Imaging statement: considerations in designing local imaging diagnostic algorithms for the COVID-19 pandemic date: 2020-05-27 pages: extension: .txt txt: ./txt/cord-297198-dneycnyr.txt cache: ./cache/cord-297198-dneycnyr.txt Content-Encoding ISO-8859-1 Content-Type text/plain; charset=ISO-8859-1 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 2 resourceName b'cord-297198-dneycnyr.txt' === file2bib.sh === id: cord-331891-a6b1xanm author: Cozzi, Diletta title: Chest X-ray in new Coronavirus Disease 2019 (COVID-19) infection: findings and correlation with clinical outcome date: 2020-06-09 pages: extension: .txt txt: ./txt/cord-331891-a6b1xanm.txt cache: ./cache/cord-331891-a6b1xanm.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 4 resourceName b'cord-331891-a6b1xanm.txt' === file2bib.sh === id: cord-337507-cqbbrnku author: Cozzi, Andrea title: Chest x-ray in the COVID-19 pandemic: Radiologists’ real-world reader performance date: 2020-09-10 pages: extension: .txt txt: ./txt/cord-337507-cqbbrnku.txt cache: ./cache/cord-337507-cqbbrnku.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-337507-cqbbrnku.txt' === file2bib.sh === id: cord-006708-nionk55w author: Aktaş, Fatma title: The pulmonary findings of Crimean–Congo hemorrhagic fever patients with chest X-ray assessments date: 2019-03-25 pages: extension: .txt txt: ./txt/cord-006708-nionk55w.txt cache: ./cache/cord-006708-nionk55w.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 4 resourceName b'cord-006708-nionk55w.txt' === file2bib.sh === id: cord-028786-400vglzm author: Oloko-Oba, Mustapha title: Diagnosing Tuberculosis Using Deep Convolutional Neural Network date: 2020-06-05 pages: extension: .txt txt: ./txt/cord-028786-400vglzm.txt cache: ./cache/cord-028786-400vglzm.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-028786-400vglzm.txt' === file2bib.sh === id: cord-266672-t85wd0xq author: Bagnera, Silvia title: Performance of Radiologists in the Evaluation of the Chest Radiography with the Use of a “new software score” in Coronavirus Disease 2019 Pneumonia Suspected Patients date: 2020-07-20 pages: extension: .txt txt: ./txt/cord-266672-t85wd0xq.txt cache: ./cache/cord-266672-t85wd0xq.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-266672-t85wd0xq.txt' === file2bib.sh === id: cord-282198-ugmv9om1 author: Pare, Joseph R. title: Point-of-care Lung Ultrasound Is More Sensitive than Chest Radiograph for Evaluation of COVID-19 date: 2020-06-19 pages: extension: .txt txt: ./txt/cord-282198-ugmv9om1.txt cache: ./cache/cord-282198-ugmv9om1.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-282198-ugmv9om1.txt' === file2bib.sh === id: cord-127759-wpqdtdjs author: Qi, Xiao title: Chest X-ray Image Phase Features for Improved Diagnosis of COVID-19 Using Convolutional Neural Network date: 2020-11-06 pages: extension: .txt txt: ./txt/cord-127759-wpqdtdjs.txt cache: ./cache/cord-127759-wpqdtdjs.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-127759-wpqdtdjs.txt' === file2bib.sh === id: cord-018027-goxdiyv3 author: Heussel, Claus Peter title: Diagnostic Radiology in Hematological Patients with Febrile Neutropenia date: 2014-11-27 pages: extension: .txt txt: ./txt/cord-018027-goxdiyv3.txt cache: ./cache/cord-018027-goxdiyv3.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 2 resourceName b'cord-018027-goxdiyv3.txt' === file2bib.sh === id: cord-350636-ufwfitue author: Shumilov, Evgenii title: Comparison of Chest Ultrasound and Standard X-Ray Imaging in COVID-19 Patients date: 2020-09-02 pages: extension: .txt txt: ./txt/cord-350636-ufwfitue.txt cache: ./cache/cord-350636-ufwfitue.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-350636-ufwfitue.txt' === file2bib.sh === id: cord-303483-wendrxee author: Rubin, Geoffrey D. title: The Role of Chest Imaging in Patient Management during the COVID-19 Pandemic: A Multinational Consensus Statement from the Fleischner Society date: 2020-04-07 pages: extension: .txt txt: ./txt/cord-303483-wendrxee.txt cache: ./cache/cord-303483-wendrxee.txt Content-Encoding ISO-8859-1 Content-Type text/plain; charset=ISO-8859-1 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 2 resourceName b'cord-303483-wendrxee.txt' === file2bib.sh === id: cord-310228-bqpvykce author: Borkowski, A. A. title: Using Artificial Intelligence for COVID-19 Chest X-ray Diagnosis date: 2020-05-26 pages: extension: .txt txt: ./txt/cord-310228-bqpvykce.txt cache: ./cache/cord-310228-bqpvykce.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-310228-bqpvykce.txt' === file2bib.sh === id: cord-345528-rk16pt0i author: Yasar, Y. title: MantisCOVID: Rapid X-Ray Chest Radiograph and Mortality Rate Evaluation With Artificial Intelligence For COVID-19 date: 2020-05-08 pages: extension: .txt txt: ./txt/cord-345528-rk16pt0i.txt cache: ./cache/cord-345528-rk16pt0i.txt Content-Encoding ISO-8859-1 Content-Type text/plain; charset=ISO-8859-1 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-345528-rk16pt0i.txt' === file2bib.sh === id: cord-013065-oj0wsstz author: Rodríguez-Fanjul, Javier title: Procalcitonin and lung ultrasound algorithm to diagnose severe pneumonia in critical paediatric patients (PROLUSP study). A randomised clinical trial date: 2020-10-08 pages: extension: .txt txt: ./txt/cord-013065-oj0wsstz.txt cache: ./cache/cord-013065-oj0wsstz.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-013065-oj0wsstz.txt' === file2bib.sh === id: cord-346942-88l03lf0 author: Kerpel, Ariel title: Diagnostic and Prognostic Value of Chest Radiographs for COVID-19 at Presentation date: 2020-08-17 pages: extension: .txt txt: ./txt/cord-346942-88l03lf0.txt cache: ./cache/cord-346942-88l03lf0.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-346942-88l03lf0.txt' === file2bib.sh === id: cord-292341-uo54ghf3 author: Cocconcelli, Elisabetta title: Clinical Features and Chest Imaging as Predictors of Intensity of Care in Patients with COVID-19 date: 2020-09-16 pages: extension: .txt txt: ./txt/cord-292341-uo54ghf3.txt cache: ./cache/cord-292341-uo54ghf3.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-292341-uo54ghf3.txt' === file2bib.sh === id: cord-312251-t6omrr07 author: Vancheri, Sergio Giuseppe title: Radiographic findings in 240 patients with COVID-19 pneumonia: time-dependence after the onset of symptoms date: 2020-05-30 pages: extension: .txt txt: ./txt/cord-312251-t6omrr07.txt cache: ./cache/cord-312251-t6omrr07.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-312251-t6omrr07.txt' === file2bib.sh === id: cord-297396-r1p7xn3a author: Ng, Ming-Yen title: Development and Validation of Risk Prediction Models for COVID-19 Positivity in a Hospital Setting date: 2020-09-15 pages: extension: .txt txt: ./txt/cord-297396-r1p7xn3a.txt cache: ./cache/cord-297396-r1p7xn3a.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-297396-r1p7xn3a.txt' === file2bib.sh === id: cord-275974-uqd30v7b author: Shorfuzzaman, Mohammad title: MetaCOVID: A Siamese neural network framework with contrastive loss for n-shot diagnosis of COVID-19 patients date: 2020-10-17 pages: extension: .txt txt: ./txt/cord-275974-uqd30v7b.txt cache: ./cache/cord-275974-uqd30v7b.txt Content-Encoding ISO-8859-1 Content-Type text/plain; charset=ISO-8859-1 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-275974-uqd30v7b.txt' === file2bib.sh === id: cord-006683-7rsmbk3j author: Coppola, M. title: Influenza A virus: radiological and clinical findings of patients hospitalised for pandemic H1N1 influenza date: 2011-01-12 pages: extension: .txt txt: ./txt/cord-006683-7rsmbk3j.txt cache: ./cache/cord-006683-7rsmbk3j.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 4 resourceName b'cord-006683-7rsmbk3j.txt' === file2bib.sh === id: cord-336843-c0sr3six author: Gerritsen, M. G. title: Improving early diagnosis of pulmonary infections in patients with febrile neutropenia using low-dose chest computed tomography date: 2017-02-24 pages: extension: .txt txt: ./txt/cord-336843-c0sr3six.txt cache: ./cache/cord-336843-c0sr3six.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-336843-c0sr3six.txt' === file2bib.sh === id: cord-103840-2diao7zh author: Mungai, B. N. title: It's not TB but what could it be? Abnormalities on chest X-rays taken during the Kenya National Tuberculosis Prevalence Survey date: 2020-08-22 pages: extension: .txt txt: ./txt/cord-103840-2diao7zh.txt cache: ./cache/cord-103840-2diao7zh.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 2 resourceName b'cord-103840-2diao7zh.txt' === file2bib.sh === id: cord-269014-ck27fm58 author: Vo, Luan Nguyen Quang title: Enhanced Private Sector Engagement for Tuberculosis Diagnosis and Reporting through an Intermediary Agency in Ho Chi Minh City, Viet Nam date: 2020-09-14 pages: extension: .txt txt: ./txt/cord-269014-ck27fm58.txt cache: ./cache/cord-269014-ck27fm58.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 4 resourceName b'cord-269014-ck27fm58.txt' === file2bib.sh === id: cord-327257-doygrgrc author: Zhu, Jocelyn title: Deep transfer learning artificial intelligence accurately stages COVID-19 lung disease severity on portable chest radiographs date: 2020-07-28 pages: extension: .txt txt: ./txt/cord-327257-doygrgrc.txt cache: ./cache/cord-327257-doygrgrc.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-327257-doygrgrc.txt' === file2bib.sh === id: cord-157444-huvnyali author: Nabulsi, Zaid title: Deep Learning for Distinguishing Normal versus Abnormal Chest Radiographs and Generalization to Unseen Diseases date: 2020-10-22 pages: extension: .txt txt: ./txt/cord-157444-huvnyali.txt cache: ./cache/cord-157444-huvnyali.txt Content-Encoding ISO-8859-1 Content-Type text/plain; charset=ISO-8859-1 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 2 resourceName b'cord-157444-huvnyali.txt' === file2bib.sh === id: cord-355218-eici4eit author: Punn, Narinder Singh title: Automated diagnosis of COVID-19 with limited posteroanterior chest X-ray images using fine-tuned deep neural networks date: 2020-10-17 pages: extension: .txt txt: ./txt/cord-355218-eici4eit.txt cache: ./cache/cord-355218-eici4eit.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 4 resourceName b'cord-355218-eici4eit.txt' === file2bib.sh === id: cord-296208-uy1r6lt2 author: Greenspan, Hayit title: Position paper on COVID-19 imaging and AI: from the clinical needs and technological challenges to initial AI solutions at the lab and national level towards a new era for AI in healthcare date: 2020-08-19 pages: extension: .txt txt: ./txt/cord-296208-uy1r6lt2.txt cache: ./cache/cord-296208-uy1r6lt2.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-296208-uy1r6lt2.txt' === file2bib.sh === id: cord-334495-7y1la856 author: Agricola, Eustachio title: Heart and Lung Multimodality Imaging in COVID-19 date: 2020-06-24 pages: extension: .txt txt: ./txt/cord-334495-7y1la856.txt cache: ./cache/cord-334495-7y1la856.txt Content-Encoding ISO-8859-1 Content-Type text/plain; charset=ISO-8859-1 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-334495-7y1la856.txt' === file2bib.sh === id: cord-294557-4h0sybiy author: Stogiannos, N. title: Coronavirus disease 2019 (COVID-19) in the radiology department: What radiographers need to know date: 2020-06-04 pages: extension: .txt txt: ./txt/cord-294557-4h0sybiy.txt cache: ./cache/cord-294557-4h0sybiy.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-294557-4h0sybiy.txt' === file2bib.sh === id: cord-347691-ia2i8svg author: Larici, Anna Rita title: Multimodality imaging of COVID-19 pneumonia: from diagnosis to follow-up. A comprehensive review date: 2020-08-17 pages: extension: .txt txt: ./txt/cord-347691-ia2i8svg.txt cache: ./cache/cord-347691-ia2i8svg.txt Content-Encoding ISO-8859-1 Content-Type text/plain; charset=ISO-8859-1 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 2 resourceName b'cord-347691-ia2i8svg.txt' === file2bib.sh === id: cord-167889-um3djluz author: Chen, Jianguo title: A Survey on Applications of Artificial Intelligence in Fighting Against COVID-19 date: 2020-07-04 pages: extension: .txt txt: ./txt/cord-167889-um3djluz.txt cache: ./cache/cord-167889-um3djluz.txt Content-Encoding ISO-8859-1 Content-Type text/plain; charset=ISO-8859-1 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 3 resourceName b'cord-167889-um3djluz.txt' === file2bib.sh === id: cord-015352-2d02eq3y author: nan title: ESPR 2017 date: 2017-04-26 pages: extension: .txt txt: ./txt/cord-015352-2d02eq3y.txt cache: ./cache/cord-015352-2d02eq3y.txt Content-Encoding UTF-8 Content-Type text/plain; charset=UTF-8 X-Parsed-By ['org.apache.tika.parser.DefaultParser', 'org.apache.tika.parser.csv.TextAndCSVParser'] X-TIKA:content_handler ToTextContentHandler X-TIKA:embedded_depth 0 X-TIKA:parse_time_millis 8 resourceName b'cord-015352-2d02eq3y.txt' Que is empty; done keyword-cxr-cord === reduce.pl bib === id = cord-103840-2diao7zh author = Mungai, B. N. title = It's not TB but what could it be? Abnormalities on chest X-rays taken during the Kenya National Tuberculosis Prevalence Survey date = 2020-08-22 pages = extension = .txt mime = text/plain words = 5916 sentences = 356 flesch = 51 summary = The World Health Organization (WHO) recommends the use of chest X-ray (CXR) as a mass screening tool in TB prevalence surveys and active case finding activities to identify patients eligible for bacteriological investigation. We systematically searched MEDLINE, CINHAL, Global Health and Google scholar databases from 1940-2019 to identify studies that described the prevalence of non-TB CXR findings during TB prevalence surveys or mass screening activities. The main finding from this analysis of X-ray images from the 2016 Kenya TB prevalence survey was that the use of CXR for TB population-based studies identified a large number of patients with abnormalities, including noncommunicable diseases (NCDs) such as cardiovascular abnormalities and chronic respiratory diseases that require clinical attention. Clinically relevant cardiac and chronic pulmonary diseases accounted for 66% of the non-TB abnormalities in our setting.To our knowledge, this is the first study in sub-Saharan Africa to characterise and quantify non-TB CXR findings among participants who underwent mass screening as part of a population-based TB prevalence survey. cache = ./cache/cord-103840-2diao7zh.txt txt = ./txt/cord-103840-2diao7zh.txt === reduce.pl bib === id = cord-157444-huvnyali author = Nabulsi, Zaid title = Deep Learning for Distinguishing Normal versus Abnormal Chest Radiographs and Generalization to Unseen Diseases date = 2020-10-22 pages = extension = .txt mime = text/plain words = 6802 sentences = 326 flesch = 44 summary = In this work, we evaluated the DLS's performance on 6 independent test sets consisting of different patient populations, spanning three countries, and with two unseen diseases (TB and COVID-19). However, as other acute diseases may share a similar clinical presentation, many cases negative for COVID-19 exhibited abnormal CXR findings that likely triggered the DLS ( Figure 5, Supplementary Figure 5 ). Finally, to facilitate the continued development of AI models for chest radiography, we are releasing our abnormal versus normal labels from 3 radiologists (2430 labels on 810 images) for the publicly-available CXR-14 test set. Two datasets were used to evaluate the DLS's performance in distinguishing normal and abnormal findings in a general abnormality detection setting. To compare the DLS with radiologists in classifying CXRs as normal versus abnormal, additional radiologists reviewed all test images without referencing additional clinical or patient data. cache = ./cache/cord-157444-huvnyali.txt txt = ./txt/cord-157444-huvnyali.txt === reduce.pl bib === id = cord-292341-uo54ghf3 author = Cocconcelli, Elisabetta title = Clinical Features and Chest Imaging as Predictors of Intensity of Care in Patients with COVID-19 date = 2020-09-16 pages = extension = .txt mime = text/plain words = 5195 sentences = 249 flesch = 47 summary = Univariate logistic regression analysis of factors associated with level of care revealed that sex, age, smoking history, FiO2, pO2 in room air at admission, bacterial co-infections developed during hospitalization, CVDs, metabolic and oncologic diseases and chest X-ray global score had significant positive association with a higher level of care in the entire study population (Table 3) . Univariate logistic regression analysis of factors associated with level of care revealed that sex, age, smoking history, FiO2, pO2 in room air at admission, bacterial co-infections developed during hospitalization, CVDs, metabolic and oncologic diseases and chest X-ray global score had significant positive association with a higher level of care in the entire study population (Table 3) . This is a retrospective analysis of clinical features and radiographic severity scores in patients with COVID-19 and how these parameters on hospital admission correlate with different levels of medical care (i.e., HIMC vs. cache = ./cache/cord-292341-uo54ghf3.txt txt = ./txt/cord-292341-uo54ghf3.txt === reduce.pl bib === id = cord-238881-tupom7fb author = Yeh, Chun-Fu title = A Cascaded Learning Strategy for Robust COVID-19 Pneumonia Chest X-Ray Screening date = 2020-04-24 pages = extension = .txt mime = text/plain words = 3356 sentences = 179 flesch = 56 summary = Although the recent international joint effort on making the availability of all sorts of open data, the public collection of CXR images is still relatively small for reliably training a deep neural network (DNN) to carry out COVID-19 prediction. Although the recent international joint effort on making the availability of all sorts of open data, the public collection of CXR images is still relatively small for reliably training a deep neural network (DNN) to carry out COVID-19 prediction. Our approach leverages a large CXR image dataset of non-COVID-19 pneumonia to generalize the original well-trained classification model via a cascaded learning scheme. Our approach leverages a large CXR image dataset of non-COVID-19 pneumonia to generalize the original well-trained classification model via a cascaded learning scheme. To extend the model, we collaborate with several medical research centers in Taiwan to collect chest x-ray images from COVID-19 patients at various stages, and re-train the pneumonia classification system using a three-stage cascaded learning strategy. cache = ./cache/cord-238881-tupom7fb.txt txt = ./txt/cord-238881-tupom7fb.txt === reduce.pl bib === id = cord-006708-nionk55w author = Aktaş, Fatma title = The pulmonary findings of Crimean–Congo hemorrhagic fever patients with chest X-ray assessments date = 2019-03-25 pages = extension = .txt mime = text/plain words = 3583 sentences = 212 flesch = 55 summary = MATERIALS AND METHODS: A total of 165 patients who were diagnosed with CCHF and examined through chest X-ray (CXR) due to respiratory symptoms at their first examination and/or during their hospitalization were included in this study. CONCLUSION: According to the results of our study, it can be suggested that radiological examination in lungs should be performed primarily with CXR and pulmonary involvement (pleural effusion and consolidation) affects survival in CCHF negatively. As a result of CXR findings obtained based on the first examination and clinical follow-up within the first 5 days, consolidation in 74 patients (44.8%), pleural effusion in 64 patients (39.8%), ground glass opacity in 49 patients (29.7%), and atelectasis in 30 patients (18.2%) were detected (Fig. 2) . In a study performed on dengue hemorrhagic fever, a total of 468 CXR taken from 363 patients were examined and parenchymal infiltration and pleural effusion were observed in more than half of the patients on the third day of follow-up. cache = ./cache/cord-006708-nionk55w.txt txt = ./txt/cord-006708-nionk55w.txt === reduce.pl bib === id = cord-331891-a6b1xanm author = Cozzi, Diletta title = Chest X-ray in new Coronavirus Disease 2019 (COVID-19) infection: findings and correlation with clinical outcome date = 2020-06-09 pages = extension = .txt mime = text/plain words = 2916 sentences = 144 flesch = 47 summary = MATERIALS AND METHODS: This is a retrospective study involving patients with clinical-epidemiological suspect of COVID-19 infection, who performed CXRs at the emergency department (ED) of our University Hospital from March 1 to March 31, 2020. Radiological evaluation of patients with clinical-epidemiological suspect of COVID-19 is mandatory, especially in the emergency department (ED) while waiting for RT-PCR results, in order to have a rapid evaluation of thoracic involvement. Therefore, the purpose of our study is to better understand the main radiographic features of COVID-19 pneumonia, by describing the main CXR findings in a selected cohort of patients, also correlating the radiological appearance with RT-PCR examination and patients outcome (intended as discharged or hospitalized into a medicine department or intensive care unit). An independent and retrospective review of each CXR was performed by two thoracic radiologists in order to define the number of radiological suspects of COVID-19 infection; after this, they defined the predominant pattern of COVID-19 pneumonia presentation in patients with a positive RT-PCR. cache = ./cache/cord-331891-a6b1xanm.txt txt = ./txt/cord-331891-a6b1xanm.txt === reduce.pl bib === id = cord-266672-t85wd0xq author = Bagnera, Silvia title = Performance of Radiologists in the Evaluation of the Chest Radiography with the Use of a “new software score” in Coronavirus Disease 2019 Pneumonia Suspected Patients date = 2020-07-20 pages = extension = .txt mime = text/plain words = 3053 sentences = 137 flesch = 46 summary = title: Performance of Radiologists in the Evaluation of the Chest Radiography with the Use of a "new software score" in Coronavirus Disease 2019 Pneumonia Suspected Patients OBJECTIVES: The purpose of this study is to assess the performance of radiologists using a new software called "COVID-19 score" when performing chest radiography on patients potentially infected by coronavirus disease 2019 (COVID-19) pneumonia. MATERIAL AND METHODS: From February–April 2020, 14 radiologists retrospectively evaluated a pool of 312 chest X-ray exams to test a new software function for lung imaging analysis based on radiological features and graded on a three-point scale. To evaluate a new tool called "COVID-19 score" made available to radiologists for lung imaging analysis, we retrospectively included in the study patients who underwent at least two consecutive chest X-rays for a total of 312 exams. In this study, we tested a new software application called "COVID-19 score" that can be used in the reporting of chest X-ray imaging in patients suspected COVID-19, based on radiological semantic features. cache = ./cache/cord-266672-t85wd0xq.txt txt = ./txt/cord-266672-t85wd0xq.txt === reduce.pl bib === id = cord-127759-wpqdtdjs author = Qi, Xiao title = Chest X-ray Image Phase Features for Improved Diagnosis of COVID-19 Using Convolutional Neural Network date = 2020-11-06 pages = extension = .txt mime = text/plain words = 3896 sentences = 250 flesch = 50 summary = In this study, we design a novel multi-feature convolutional neural network (CNN) architecture for multi-class improved classification of COVID-19 from CXR images. In this work we show how local phase CXR features based image enhancement improves the accuracy of CNN architectures for COVID-19 diagnosis. Our proposed method is designed for processing CXR images and consists of two main stages as illustrated in Figure 1 : 1-We enhance the CXR images (CXR(x, y)) using local phase-based image processing method in order to obtain a multi-feature CXR image (M F (x, y)), and 2-we classify CXR(x, y) by designing a deep learning approach where multi feature CXR images (M F (x, y)), together with original CXR data (CXR(x, y)), is used for improving the classification performance. Our proposed multi-feature CNN architectures were trained on a large dataset in terms of the number of COVID-19 CXR scans and have achieved improved classification accuracy across all classes. cache = ./cache/cord-127759-wpqdtdjs.txt txt = ./txt/cord-127759-wpqdtdjs.txt === reduce.pl bib === id = cord-337507-cqbbrnku author = Cozzi, Andrea title = Chest x-ray in the COVID-19 pandemic: Radiologists’ real-world reader performance date = 2020-09-10 pages = extension = .txt mime = text/plain words = 2594 sentences = 114 flesch = 47 summary = METHODS: In this retrospective observational study we enrolled all patients presenting to the emergency department of a Milan-based university hospital from February 24th to April 8th 2020 who underwent nasopharyngeal swab for reverse transcriptase-polymerase chain reaction (RT-PCR) and anteroposterior bedside CXR within 12 h. The two largest by far are a retrospective review by a single radiologist of 518 CXRs acquired during the first phase of the pandemic peak (from March 1 st to March 15 th )with a resulting overall sensitivity of 57% [22] and a study coming from our group and performed on 535 patients [23] . Real-world data from this study, albeit conducted in a highprevalence region and during a SARS-CoV-2 pandemic peak, seem to provide a better scenario, in which radiologists with less than 10 years of experience matched the 89.0% sensitivity attained by radiologists with more than 10 years of experience, with similar disease prevalence in the CXR subsets read by each group (73% versus 77%, respectively). cache = ./cache/cord-337507-cqbbrnku.txt txt = ./txt/cord-337507-cqbbrnku.txt === reduce.pl bib === id = cord-275974-uqd30v7b author = Shorfuzzaman, Mohammad title = MetaCOVID: A Siamese neural network framework with contrastive loss for n-shot diagnosis of COVID-19 patients date = 2020-10-17 pages = extension = .txt mime = text/plain words = 5429 sentences = 268 flesch = 49 summary = In summary, following are the contributions of our work: (a) A meta learning framework called MetaCOVID based on Siamese neural network is presented for diagnosis of COVID-19 patients from chest X-ray images, (b) The proposed work focuses on the benefit of using contrastive loss and n-shot learning in framework design, (c) A fine-tuned pre-trained VGG encoder is used to capture unbiased feature representations to improve feature embeddings from the input images, (d) The COVID-19 diagnosis problem is formulated as a k-way, n-shot classification problem where k and n represent the number of class labels and data samples used for model training, (e) Performance evaluation is presented to demonstrate the efficacy of the proposed framework with a limited dataset. In contrast, we have proposed an end-to-end trainable nshot deep meta learning framework based on Siamese neural network to classify COVID-19 cases with limited training CXR images. cache = ./cache/cord-275974-uqd30v7b.txt txt = ./txt/cord-275974-uqd30v7b.txt === reduce.pl bib === id = cord-018027-goxdiyv3 author = Heussel, Claus Peter title = Diagnostic Radiology in Hematological Patients with Febrile Neutropenia date = 2014-11-27 pages = extension = .txt mime = text/plain words = 4904 sentences = 306 flesch = 40 summary = Clinically, lungs are affected in 30 % of febrile neutropenic patients and allogeneic hematopoietic stem cell transplant (aSCT) recipients, paranasal sinuses in 3 % of neutropenic patients, and 30 % in the aSCT setting (concomitant to pneumonia), while the gastrointestinal tract, liver, spleen, central nervous system, and kidneys are less frequently involved [ 4 ] . While CXR provides relevant clinical information concerning central venous catheters (CVC), pleural effusion, and pulmonary congestion [ 17 ] , it fails to enable early detection or exclusion of pneumonia, which is a major task in immunocompromised hosts. For use in the context of clinical and epidemiological research in neutropenic patients, standards for the interpretation of radiological fi ndings in invasive fungal infections have been elaborated [ 10 , 51 ] ; newly emerged "typical" CT patterns (dense, well-circumscribed lesions with or without a halo sign, air-crescent sign) are classifi ed as a clinical criterion for fungal pneumonia Figs. cache = ./cache/cord-018027-goxdiyv3.txt txt = ./txt/cord-018027-goxdiyv3.txt === reduce.pl bib === id = cord-296208-uy1r6lt2 author = Greenspan, Hayit title = Position paper on COVID-19 imaging and AI: from the clinical needs and technological challenges to initial AI solutions at the lab and national level towards a new era for AI in healthcare date = 2020-08-19 pages = extension = .txt mime = text/plain words = 8008 sentences = 395 flesch = 47 summary = We focus on three specific use-cases for which AI systems can be built: early disease detection, management in a hospital setting, and building patient-specific predictive models that require the combination of imaging with additional clinical data. Many studies have emerged in the last several months from the medical imaging community with many research groups as well as companies introducing deep learning based solutions to tackle the various tasks: mostly in detection of the disease (vs normal), and more recently also for staging disease severity. In Section 2 of this paper we focus on three specific use-cases for which AI systems can be built: detection, patient management, and predictive models in which the imaging is combined with additional clinical features. Rapid ai development cycle for the coronavirus (covid-19) pandemic: Initial results for automated detection and patient monitoring using deep learning ct image analysis cache = ./cache/cord-296208-uy1r6lt2.txt txt = ./txt/cord-296208-uy1r6lt2.txt === reduce.pl bib === id = cord-028786-400vglzm author = Oloko-Oba, Mustapha title = Diagnosing Tuberculosis Using Deep Convolutional Neural Network date = 2020-06-05 pages = extension = .txt mime = text/plain words = 2438 sentences = 118 flesch = 44 summary = We propose a Computer-Aided Detection model using Deep Convolutional Neural Networks to automatically detect TB from Montgomery County (MC) Tuberculosis radiographs. As a result, to profer solution to the issue of limited or lack of expert radiologist and misdiagnosis of CXR, we propose a Deep Convolutional Neural Networks (CNN) model that will automatically diagnose large numbers of CXR at a time for TB manifestation in developing regions where TB is most prevalent. A model based on Deep Convolutional Neural Network (CNN) structure has been proposed in this work for the detection and classification of Tuberculosis. Presented in this paper is a model that aids early detection of Tuberculosis using CNN structure to automatically extract distinctive features from chest radiographs and classify them into normal and abnormal categories. TX-CNN: detecting tuberculosis in chest X-ray images using convolutional neural network cache = ./cache/cord-028786-400vglzm.txt txt = ./txt/cord-028786-400vglzm.txt === reduce.pl bib === id = cord-303483-wendrxee author = Rubin, Geoffrey D. title = The Role of Chest Imaging in Patient Management during the COVID-19 Pandemic: A Multinational Consensus Statement from the Fleischner Society date = 2020-04-07 pages = extension = .txt mime = text/plain words = 4315 sentences = 189 flesch = 37 summary = Thoracic imaging with chest radiography (CXR) and computed tomography (CT) are key tools for pulmonary disease diagnosis and management, but their role in the management of COVID-19 has not been considered within the multivariable context of the severity of respiratory disease, pre-test probability, risk factors for disease progression, and critical resource constraints. Thoracic imaging with chest radiography (CXR) and computed tomography (CT) are key tools for pulmonary disease diagnosis and management, but their role in the management of COVID-19 has not been considered within the multivariable context of the severity of respiratory disease, pre-test probability, risk factors for disease progression, and critical resource constraints. The severity of respiratory disease and pre-test probability of COVID-19 infection are specified for each scenario, with additional key considerations including the presence of risk factors for disease progression, evidence of disease progression, and the presence of significant critical resource constraints ( Table 1) . cache = ./cache/cord-303483-wendrxee.txt txt = ./txt/cord-303483-wendrxee.txt === reduce.pl bib === id = cord-282198-ugmv9om1 author = Pare, Joseph R. title = Point-of-care Lung Ultrasound Is More Sensitive than Chest Radiograph for Evaluation of COVID-19 date = 2020-06-19 pages = extension = .txt mime = text/plain words = 3406 sentences = 193 flesch = 51 summary = Our primary objective was to determine whether lung ultrasound (LUS) B-lines, when excluding patients with alternative etiologies for B-lines, are more sensitive for the associated diagnosis of COVID-19 than CXR. METHODS: This was a retrospective cohort study of all patients who presented to a single, academic emergency department in the United States between March 20 and April 6, 2020, and received LUS, CXR, and viral testing for COVID-19 as part of their diagnostic evaluation. Lung ultrasound (LUS) has been shown to outperform chest radiograph (CXR) in its ability to detect abnormalities with non-coronavirus disease 2019 (COVID-19) pulmonary infections. This was a retrospective, observational, cohort study of patients undergoing COVID-19 testing (based on real-time reverse transcriptase-polymerase chain reaction [RT-PCR] of nasopharyngeal sampling performed on an assay developed by the Center for Regenerative Medicine at Boston University, operating under an Emergency Use Authorization], who also had both diagnostic LUS and CXR for the evaluation of COVID-19 in the emergency department (ED). cache = ./cache/cord-282198-ugmv9om1.txt txt = ./txt/cord-282198-ugmv9om1.txt === reduce.pl bib === id = cord-013065-oj0wsstz author = Rodríguez-Fanjul, Javier title = Procalcitonin and lung ultrasound algorithm to diagnose severe pneumonia in critical paediatric patients (PROLUSP study). A randomised clinical trial date = 2020-10-08 pages = extension = .txt mime = text/plain words = 3735 sentences = 225 flesch = 44 summary = title: Procalcitonin and lung ultrasound algorithm to diagnose severe pneumonia in critical paediatric patients (PROLUSP study). Besides this, the use of biomarkers such as procalcitonin (PCT) has become more widespread during the past 10 years, helping clinicians diagnose bacterial etiology, especially in patients who have only had a fever for a few hours or those admitted to intensive care units [16] [17] [18] [19] [20] . Therefore, we propose this clinical trial, based on combining LUS and PCT in an algorithm with the aim to improve quality of care in children with pneumonia in a PICU. The use procalcitonin and lung ultrasound algorithm will help us diagnose bacterial pneumonia accurately and prescribe the correct antibiotic treatment. This clinical trial is focused on improving the quality of care for paediatric patients with suspected bacterial pneumonia. This clinical trial is focused on improving the quality of care for paediatric patients with suspected bacterial pneumonia. cache = ./cache/cord-013065-oj0wsstz.txt txt = ./txt/cord-013065-oj0wsstz.txt === reduce.pl bib === id = cord-269014-ck27fm58 author = Vo, Luan Nguyen Quang title = Enhanced Private Sector Engagement for Tuberculosis Diagnosis and Reporting through an Intermediary Agency in Ho Chi Minh City, Viet Nam date = 2020-09-14 pages = extension = .txt mime = text/plain words = 5040 sentences = 238 flesch = 49 summary = title: Enhanced Private Sector Engagement for Tuberculosis Diagnosis and Reporting through an Intermediary Agency in Ho Chi Minh City, Viet Nam We tabulated descriptive statistics for private provider engagement and participation, the number and proportion of referred people progressing through the study's TB care cascade by intervention district and the private TB treatment reported to our study. The study identified 1203 people with TB of whom 7.6% (91/1203) were referred and linked to care with the NTP (Figure 2 ), while 92.4% (1112/1203) consisted of private TB treatment reports and remained un-notified (Table 3) . Table 4 and Figure 3 summarize changes in the NTP's TB case notifications in the study's intervention area and present the modeled impact of including private TB treatment on official notification statistics. Our pilot study showed that the PPIA model was effective in engaging a large number of private providers in the Vietnamese urban setting to contribute to TB care and prevention efforts. cache = ./cache/cord-269014-ck27fm58.txt txt = ./txt/cord-269014-ck27fm58.txt === reduce.pl bib === id = cord-312251-t6omrr07 author = Vancheri, Sergio Giuseppe title = Radiographic findings in 240 patients with COVID-19 pneumonia: time-dependence after the onset of symptoms date = 2020-05-30 pages = extension = .txt mime = text/plain words = 3502 sentences = 189 flesch = 48 summary = OBJECTIVE: To analyze the most frequent radiographic features of COVID-19 pneumonia and assess the effectiveness of chest X-ray (CXR) in detecting pulmonary alterations. Alteration's type (reticular/ground-glass opacity (GGO)/consolidation) and distribution (bilateral/unilateral, upper/middle/lower fields, peripheral/central) were noted. CONCLUSIONS: The most frequent lesions in COVID-19 patients were GGO (intermediate/late phase) and reticular alteration (early phase) while consolidation gradually increased over time. Our study aimed to evaluate the percentage of abnormal chest radiographs at different time intervals from the onset of symptoms and to identify the type and distribution of radiographic alterations and their frequency at different times throughout the disease course of COVID-19 pneumonia. Chest CT showed high sensitivity in detecting GGO, which is considered a typical finding in COVID-19 pneumonia and, in some cases, may be the only alteration present in the early phases of the disease [3, 16] . cache = ./cache/cord-312251-t6omrr07.txt txt = ./txt/cord-312251-t6omrr07.txt === reduce.pl bib === id = cord-334495-7y1la856 author = Agricola, Eustachio title = Heart and Lung Multimodality Imaging in COVID-19 date = 2020-06-24 pages = extension = .txt mime = text/plain words = 6791 sentences = 325 flesch = 33 summary = From a clinical point of view, cardiac involvement during COVID-19 may present a wide spectrum of severity ranging from subclinical myocardial injury to well-defined clinical entities (myocarditis, myocardial infarction, pulmonary embolism and heart failure), whose incidence and prognostic implications are currently largely unknown due to a significant lack of imaging data. The use of integrated heart and lung multimodality imaging plays a central role in different clinical settings and is essential in diagnosis, risk stratification and management of COVID-19 patients. In this context, the use of multiple diagnostic imaging techniques may apply to both heart and lung to provide an integrated assessment of cardiac and pulmonary function and to refine diagnosis, risk stratification and management of COVID-19 patients. patients not requiring ICU, when clinical presentation and biomarker alterations suggest acute-onset myocardial inflammation, if the diagnosis is likely to impact on management, CMR may be considered to confirm acute myocarditis, after exclusion of alternative relevant clinical conditions, including ACS and HF, by means of other rapidly available imaging modalities (i.e. cardiac CT scan or TTE). cache = ./cache/cord-334495-7y1la856.txt txt = ./txt/cord-334495-7y1la856.txt === reduce.pl bib === id = cord-350636-ufwfitue author = Shumilov, Evgenii title = Comparison of Chest Ultrasound and Standard X-Ray Imaging in COVID-19 Patients date = 2020-09-02 pages = extension = .txt mime = text/plain words = 2368 sentences = 137 flesch = 49 summary = We aimed to investigate patterns of ChUS in COVID-19 patients and compare the findings with results from chest X-ray (CRX). We aimed to investigate patterns of ChUS in COVID-19 patients and compare the findings with results from chest X-ray (CRX). Besides pathological B-lines and subpleural consolidations, pleural line abnormality (89 %; n = 16/18) was the third most common feature in patients with respiratory manifestations of COVID-19 detected by ChUS. Besides pathological B-lines and subpleural consolidations, pleural line abnormality (89 %; n = 16/18) was the third most common feature in patients with respiratory manifestations of COVID-19 detected by ChUS. A Clinical Study of Noninvasive Assessment of Lung Lesions in Patients with Coronavirus Disease-19 (COVID-19) by Bedside Ultrasound cache = ./cache/cord-350636-ufwfitue.txt txt = ./txt/cord-350636-ufwfitue.txt === reduce.pl bib === id = cord-294557-4h0sybiy author = Stogiannos, N. title = Coronavirus disease 2019 (COVID-19) in the radiology department: What radiographers need to know date = 2020-06-04 pages = extension = .txt mime = text/plain words = 6725 sentences = 377 flesch = 50 summary = Objectives include to: i) outline pathophysiology and basic epidemiology useful for radiographers, ii) discuss the role of medical imaging in the diagnosis of Covid-19, iii) summarise national and international guidelines of imaging Covid-19, iv) present main clinical and imaging findings and v) summarise current safety recommendations for medical imaging practice. CXR imaging of suspected or confirmed Covid-19 cases should be performed with portable equipment within specifically designated isolated rooms for eliminating the risks of cross-infection within the Radiology department. After the outbreak of the Covid-19 pandemic, many professional bodies and learned societies have been quick to issue official guidelines on how medical imaging should optimally be performed for early diagnosis and related management of these patients, but also how staff should be protected from cross-infection. Chest radiographic and CT findings of the 2019 novel Coronavirus disease (COVID-19): analysis of nine patients treated in Korea Imaging and clinical features of patients with 2019 novel coronavirus SARS-CoV-2: a systematic review and meta-analysis cache = ./cache/cord-294557-4h0sybiy.txt txt = ./txt/cord-294557-4h0sybiy.txt === reduce.pl bib === id = cord-297198-dneycnyr author = Khan, T. title = Re: a British Society of Thoracic Imaging statement: considerations in designing local imaging diagnostic algorithms for the COVID-19 pandemic date = 2020-05-27 pages = extension = .txt mime = text/plain words = 644 sentences = 61 flesch = 71 summary = In answering Question 2, the authors comment that "CXR may be abnormal in the majority of COVID-19 cases", incorrectly inferring that the study of Huang et al. in The Lancet does mistakenly state that they found "bilateral involvement of chest radiographs" in 40/41 patients in Table 2 of their results; however, from reading the main text, it is clear that the imaging method they are referring to is actually chest computed tomography (CT), not chest radiography (CXR): "On admission, abnormalities in chest CT images were detected among all patients. The study of Guan et al., also cited by Nair et al., reported CXR abnormalities in only 162/274 (59.1%) of COVID-19 patients. reported abnormal CXR in 44/64 (68.8%) of COVID-19 patients on presentation 4 ; however, it is important to note that both of these studies included hospitalised patients, representing individuals with more severe illness. reported CXR findings of 636 COVID-19 patients presenting to urgent-care centres and found that only 168 (26.4%) were reported originally as abnormal. cache = ./cache/cord-297198-dneycnyr.txt txt = ./txt/cord-297198-dneycnyr.txt === reduce.pl bib === id = cord-345528-rk16pt0i author = Yasar, Y. title = MantisCOVID: Rapid X-Ray Chest Radiograph and Mortality Rate Evaluation With Artificial Intelligence For COVID-19 date = 2020-05-08 pages = extension = .txt mime = text/plain words = 3173 sentences = 198 flesch = 58 summary = This tool delivers a rapid screening test by analyzing the X-ray Chest Radiograph scans via Artificial Intelligence (AI) and it also evaluates the mortality rate of patients with the synthesis of the patient history with the machine learning methods. A rapid analysis for the Chest X-ray (CXR) scans, CT, Infection Rate or Mortality Rate with the machine learning methods are some of the helpful tools and researchers are trying to build such tools for pre-screening COVID-19. This study defines a deployed environment 1 for rapid evaluation of the mortality rate and CXR scans via machine learning tools. The evaluation platform has two outputs after screening the group of patients as the prediction about the risk in COVID-19 via CXR and the mortality rate. . https://doi.org/10.1101/2020.05.04.20090779 doi: medRxiv preprint mantisCOVID cannot catch COVID-19 patient via AI elimination from CXR, the physician can change approaching style to the patient via evaluating the mortality rate. cache = ./cache/cord-345528-rk16pt0i.txt txt = ./txt/cord-345528-rk16pt0i.txt === reduce.pl bib === id = cord-297396-r1p7xn3a author = Ng, Ming-Yen title = Development and Validation of Risk Prediction Models for COVID-19 Positivity in a Hospital Setting date = 2020-09-15 pages = extension = .txt mime = text/plain words = 3251 sentences = 182 flesch = 53 summary = OBJECTIVES: To develop:(1) two validated risk prediction models for COVID-19 positivity using readily available parameters in a general hospital setting; (2) nomograms and probabilities to allow clinical utilisation.  Developed two simple-to use nomograms for identifying COVID-19 positive patients  Probabilities are provided to allow healthcare leaders to decide suitable cut-offs  Variables are age, white cell count, chest x-ray appearances and contact history  Model variables are easily available in the general hospital setting. To develop: (1) two validated risk prediction models for COVID-19 positivity using readily available parameters in a general hospital setting; (2) nomograms and probabilities to allow clinical utilisation. Thus, a COVID-19 prediction model based on clinical, laboratory and radiological findings which presents the sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) would allow public healthcare systems to decide a suitable strategy on prioritizing tests when such RT-PCR availability is constrained. cache = ./cache/cord-297396-r1p7xn3a.txt txt = ./txt/cord-297396-r1p7xn3a.txt === reduce.pl bib === id = cord-006683-7rsmbk3j author = Coppola, M. title = Influenza A virus: radiological and clinical findings of patients hospitalised for pandemic H1N1 influenza date = 2011-01-12 pages = extension = .txt mime = text/plain words = 4838 sentences = 374 flesch = 54 summary = The primary lesions assessed at CXR and chest CT were [13] and recent publications [10, 11] describing the main radiological pulmonary manifestations of A/H1N1 infl uenza: interstitial reticulation (RI; linear opacities of the central and peripheral interstitium appearing as radio-opaque lines on CXR and hyperdensities on CT), nodules (N; well-or illdefi ned, rounded opacities/hyperdensities, with maximum diameter of 3 cm.), ground-glass opacities (GGO; heterogeneous increase in parenchymal opacity with preservation of bronchial and vascular margins), consolidation (CONS; homogenously increased parenchymal attenuation that obscures the margins of the bronchial and vessels walls). To date, few studies addressing chest imaging in patients affected by infl uenza A/H1N1 have been published [10] [11] [12] , and the presentation of H1N1 virus pneumonia on both CXR and chest CT seems to refl ect the general features of viral pneumonia [23] . One study [11] reported on the main CXR and chest CT fi ndings in seven patients affected by infl uenza A/H1N1: bilateral GGO, more frequently associated with focal or multifocal areas of consolidation. cache = ./cache/cord-006683-7rsmbk3j.txt txt = ./txt/cord-006683-7rsmbk3j.txt === reduce.pl bib === id = cord-167889-um3djluz author = Chen, Jianguo title = A Survey on Applications of Artificial Intelligence in Fighting Against COVID-19 date = 2020-07-04 pages = extension = .txt mime = text/plain words = 12248 sentences = 768 flesch = 50 summary = The progress of CT image inspection based on AI usually includes the following steps: Region Of Interest (ROI) segmentation, lung tissue feature extraction, candidate infection region detection, and COVID-19 classification. Data sources Methods Country/region Huang [82] Yang [231] , WHO [216] CNN, LSTM, MLP, GRU China Hu [80, 81] The Paper [148] , WHO [216] MAE, clustering China Yang [233] Baidu [16] SEIR, LSTM China Fong [51, 52] NHC [139] SVM, PNN China Ai [3] WHO [54, 216] ANFIS, FPA China, USA Rizk [168] WHO [216] ISACL-MFNN USA, Italy, Spain Giuliani [62] Italy [144] EMTMGL Italy Ayyoubzadeh [14] Worldometer [218] , Google [201] LR, LSTM Iran Marini [129, 130] Swiss population Enerpol Switzerland Lai [110] IATA [126] , Worldpop [219] ML Global Punn [155] JHU CSSE [49] SVR, PR, DNN, LSTM, RNN Predicting commercially available antiviral drugs that may act on the novel coronavirus (sars-cov-2) through a drug-target interaction deep learning model cache = ./cache/cord-167889-um3djluz.txt txt = ./txt/cord-167889-um3djluz.txt === reduce.pl bib === id = cord-336843-c0sr3six author = Gerritsen, M. G. title = Improving early diagnosis of pulmonary infections in patients with febrile neutropenia using low-dose chest computed tomography date = 2017-02-24 pages = extension = .txt mime = text/plain words = 4321 sentences = 239 flesch = 49 summary = title: Improving early diagnosis of pulmonary infections in patients with febrile neutropenia using low-dose chest computed tomography We performed a prospective study in patients with chemotherapy induced febrile neutropenia to investigate the diagnostic value of low-dose computed tomography compared to standard chest radiography. Two studies comparing LDCT to CXR in patients with persistent febrile neutropenia demonstrated an increased detection of pulmonary abnormalities. The diagnosis of possible IFD in the patient with a negative LDCT scan was based on abnormalities on HRCT made on day 4 of fever. We conducted a prospective study to evaluate whether pulmonary focus detection would improve using a LDCT scan instead of CXR on the first day of febrile neutropenia. [3] In a retrospective study 1083 adult SCT patients were evaluated, but in none of the 242 CXRs performed in asymptomatic patients with febrile neutropenia pulmonary abnormalities indicative of infection were detected. cache = ./cache/cord-336843-c0sr3six.txt txt = ./txt/cord-336843-c0sr3six.txt === reduce.pl bib === id = cord-327257-doygrgrc author = Zhu, Jocelyn title = Deep transfer learning artificial intelligence accurately stages COVID-19 lung disease severity on portable chest radiographs date = 2020-07-28 pages = extension = .txt mime = text/plain words = 3686 sentences = 221 flesch = 50 summary = title: Deep transfer learning artificial intelligence accurately stages COVID-19 lung disease severity on portable chest radiographs This study employed deep-learning convolutional neural networks to stage lung disease severity of Coronavirus Disease 2019 (COVID-19) infection on portable chest x-ray (CXR) with radiologist score of disease severity as ground truth. Deep-learning convolutional neural network (CNN) was used to predict lung disease severity scores. In conclusion, deep-learning CNN accurately stages disease severity on portable chest x-ray of COVID-19 lung infection. This study tested the hypothesis that deep-learning convolutional neural networks accurately stage disease severity on portable chest x-rays using radiologists' severity scores as ground truths associated with COVID-19 lung infection. Deep-learning AI, specifically a convolutional neural network, is well suited to extract information from CXR and stage disease severity by training using chest radiologist determination of disease severity scores. In conclusion, deep-learning convolutional neural networks accurately stage lung disease severity on portable chest x-rays associated with COVID-19 lung infection. cache = ./cache/cord-327257-doygrgrc.txt txt = ./txt/cord-327257-doygrgrc.txt === reduce.pl bib === id = cord-310228-bqpvykce author = Borkowski, A. A. title = Using Artificial Intelligence for COVID-19 Chest X-ray Diagnosis date = 2020-05-26 pages = extension = .txt mime = text/plain words = 3193 sentences = 216 flesch = 51 summary = We utilized publicly available CXR images for patients with COVID-19 pneumonia, pneumonia from other etiologies, and normal CXRs as a dataset to train Microsoft CustomVision. We then validated the program using CXRs of patients from our institution with confirmed COVID-19 diagnoses along with non-COVID-19 pneumonia and normal CXRs. Our model performed with 100% sensitivity, 95% specificity, 97% accuracy, 91% positive predictive value, and 100% negative predictive value. We first trained the Microsoft CustomVision automated image classification and object detection system to differentiate cases of COVID-19 from pneumonia from other etiologies as well as normal lung CXRs. We then tested our model against known patients from our medical center. We have utilized a readily available, commercial platform to demonstrate the potential of AI to assist in the successful diagnosis of COVID-19 pneumonia on CXR images. cache = ./cache/cord-310228-bqpvykce.txt txt = ./txt/cord-310228-bqpvykce.txt === reduce.pl bib === id = cord-347691-ia2i8svg author = Larici, Anna Rita title = Multimodality imaging of COVID-19 pneumonia: from diagnosis to follow-up. A comprehensive review date = 2020-08-17 pages = extension = .txt mime = text/plain words = 7456 sentences = 363 flesch = 37 summary = The purpose of this comprehensive review is to understand the diagnostic capabilities and limitations of chest X-ray (CXR) and high-resolution computed tomography (HRCT) in defining the common imaging features of COVID-19 pneumonia and correlating them with the underlying pathogenic mechanisms. As suggested in the recently published WHO (World Health Organization) advice guide for the diagnosis and management of COVID-19, chest imaging should be used for diagnostic purpose in symptomatic patients if RT-PCR is not available or its results are delayed, or in case of negative result in the presence of a high clinical suspicion of COVID-19 [11] . Apart from recognizing COVID-19 pneumonia features, imaging -especially CT -may reveal possible alternative diagnoses (e.g. pulmonary oedema, alveolar haemorrhage, other type of lung infections) that justify patient's respiratory symptoms [25, 26] . cache = ./cache/cord-347691-ia2i8svg.txt txt = ./txt/cord-347691-ia2i8svg.txt === reduce.pl bib === id = cord-355218-eici4eit author = Punn, Narinder Singh title = Automated diagnosis of COVID-19 with limited posteroanterior chest X-ray images using fine-tuned deep neural networks date = 2020-10-17 pages = extension = .txt mime = text/plain words = 5950 sentences = 324 flesch = 48 summary = Recently, with the release of publicly available datasets of corona positive patients comprising of computed tomography (CT) and chest X-ray (CXR) imaging; scientists, researchers and healthcare experts are contributing for faster and automated diagnosis of COVID-19 by identifying pulmonary infections using deep learning approaches to achieve better cure and treatment. Following from this context, this article presents the random oversampling and weighted class loss function approach for unbiased fine-tuned learning (transfer learning) in various state-of-the-art deep learning approaches such as baseline ResNet, Inception-v3, Inception ResNet-v2, DenseNet169, and NASNetLarge to perform binary classification (as normal and COVID-19 cases) and also multi-class classification (as COVID-19, pneumonia, and normal case) of posteroanterior CXR images. [31] proposed a deep convolutional neural network based automatic prediction model of COVID-19 with the help of pre-trained transfer models using CXR images. Detection of coronavirus (covid-19) associated pneumonia based on generative adversarial networks and a fine-tuned deep transfer learning model using chest x-ray dataset cache = ./cache/cord-355218-eici4eit.txt txt = ./txt/cord-355218-eici4eit.txt === reduce.pl bib === id = cord-346942-88l03lf0 author = Kerpel, Ariel title = Diagnostic and Prognostic Value of Chest Radiographs for COVID-19 at Presentation date = 2020-08-17 pages = extension = .txt mime = text/plain words = 4481 sentences = 248 flesch = 52 summary = The purpose of this study was to assess the diagnostic and prognostic value of chest radiographs (CXR) for coronavirus disease 2019 (COVID-19) at presentation. 13 Data on the strengths and weaknesses of chest radiography for the diagnosis of COVID-19 are important, as CXRs are the most commonly used triage imaging tool in any patient presenting with respiratory symptoms. We identified our study population by extracting severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RT-PCR test results (positive or negative) of nasopharyngeal swabs from all consecutive patients older than 18 years analyzed at our hospital's laboratory from the ED from March 6-31, 2020, who had a CXR at presentation (within 24 hours of the first RT-PCR). When the RALE score was evaluated as a prognostic indicator within the COVID-19 patient group, both readers had statistically significant discriminatory accuracy for severe disease and poor outcomes (Table 3) . cache = ./cache/cord-346942-88l03lf0.txt txt = ./txt/cord-346942-88l03lf0.txt === reduce.pl bib === id = cord-015352-2d02eq3y author = nan title = ESPR 2017 date = 2017-04-26 pages = extension = .txt mime = text/plain words = 82253 sentences = 4479 flesch = 46 summary = Lapierre; Montreal/CA Summary: Objectives: To review the classification of visceroatrial situs To describe the associated cardiac and non-cardiac anomalies To illustrate typical findings in fetuses, neonates and children To discuss the surgical consideration and the long-term follow-up in these patients Abstract: By definition, the type of situs is determined by the relationship between the atria and the adjacent organs. As is often the case, radiology in JIA is all about: knowing your clinicians (i.e. the pretest likelihood for disease) being technically eloquent (e.g. using high-resolution US probes, not delaying post-contrast MRI acquisitions) knowing what is normal (e.g. normal undulations in the articular surface, focal bone marrow signal variation) not being dogmatic about individual observations or measurements interpreting your findings in a clinical context The lecture will demonstrate similarities and differences among joints and modalities in children with variable-severity JIA. cache = ./cache/cord-015352-2d02eq3y.txt txt = ./txt/cord-015352-2d02eq3y.txt ===== Reducing email addresses cord-296208-uy1r6lt2 Creating transaction Updating adr table ===== Reducing keywords cord-103840-2diao7zh cord-157444-huvnyali cord-292341-uo54ghf3 cord-006708-nionk55w cord-238881-tupom7fb cord-331891-a6b1xanm cord-266672-t85wd0xq cord-127759-wpqdtdjs cord-337507-cqbbrnku cord-275974-uqd30v7b cord-018027-goxdiyv3 cord-296208-uy1r6lt2 cord-303483-wendrxee cord-028786-400vglzm cord-282198-ugmv9om1 cord-013065-oj0wsstz cord-269014-ck27fm58 cord-312251-t6omrr07 cord-334495-7y1la856 cord-350636-ufwfitue cord-294557-4h0sybiy cord-297198-dneycnyr cord-345528-rk16pt0i cord-006683-7rsmbk3j cord-297396-r1p7xn3a cord-167889-um3djluz cord-327257-doygrgrc cord-336843-c0sr3six cord-310228-bqpvykce cord-347691-ia2i8svg cord-346942-88l03lf0 cord-355218-eici4eit cord-015352-2d02eq3y Creating transaction Updating wrd table ===== Reducing urls cord-103840-2diao7zh cord-157444-huvnyali cord-296208-uy1r6lt2 cord-028786-400vglzm cord-303483-wendrxee cord-013065-oj0wsstz cord-282198-ugmv9om1 cord-345528-rk16pt0i cord-327257-doygrgrc cord-310228-bqpvykce cord-346942-88l03lf0 Creating transaction Updating url table ===== Reducing named entities cord-157444-huvnyali cord-292341-uo54ghf3 cord-006708-nionk55w cord-238881-tupom7fb cord-331891-a6b1xanm cord-103840-2diao7zh cord-266672-t85wd0xq cord-127759-wpqdtdjs cord-337507-cqbbrnku cord-275974-uqd30v7b cord-018027-goxdiyv3 cord-296208-uy1r6lt2 cord-028786-400vglzm cord-303483-wendrxee cord-282198-ugmv9om1 cord-269014-ck27fm58 cord-013065-oj0wsstz cord-312251-t6omrr07 cord-334495-7y1la856 cord-350636-ufwfitue cord-006683-7rsmbk3j cord-297198-dneycnyr cord-294557-4h0sybiy cord-345528-rk16pt0i cord-297396-r1p7xn3a cord-327257-doygrgrc cord-347691-ia2i8svg cord-167889-um3djluz cord-336843-c0sr3six cord-310228-bqpvykce cord-355218-eici4eit cord-346942-88l03lf0 cord-015352-2d02eq3y Creating transaction Updating ent table ===== Reducing parts of speech cord-331891-a6b1xanm cord-006708-nionk55w cord-238881-tupom7fb cord-103840-2diao7zh cord-157444-huvnyali cord-292341-uo54ghf3 cord-127759-wpqdtdjs cord-266672-t85wd0xq cord-337507-cqbbrnku cord-018027-goxdiyv3 cord-275974-uqd30v7b cord-028786-400vglzm cord-303483-wendrxee cord-282198-ugmv9om1 cord-296208-uy1r6lt2 cord-013065-oj0wsstz cord-269014-ck27fm58 cord-350636-ufwfitue cord-312251-t6omrr07 cord-297198-dneycnyr cord-345528-rk16pt0i cord-297396-r1p7xn3a cord-334495-7y1la856 cord-294557-4h0sybiy cord-327257-doygrgrc cord-006683-7rsmbk3j cord-336843-c0sr3six cord-310228-bqpvykce cord-346942-88l03lf0 cord-355218-eici4eit cord-347691-ia2i8svg cord-167889-um3djluz cord-015352-2d02eq3y Creating transaction Updating pos table Building ./etc/reader.txt cord-015352-2d02eq3y cord-167889-um3djluz cord-347691-ia2i8svg cord-294557-4h0sybiy cord-347691-ia2i8svg cord-334495-7y1la856 number of items: 33 sum of words: 229,466 average size in words: 6,953 average readability score: 48 nouns: patients; disease; imaging; study; pneumonia; diagnosis; chest; cases; images; findings; lung; data; children; image; infection; model; results; patient; learning; detection; case; time; age; score; sensitivity; analysis; years; radiologists; coronavirus; symptoms; treatment; ray; studies; evaluation; risk; ultrasound; abnormalities; system; test; classification; features; use; group; lesions; care; contrast; number; role; accuracy; dataset verbs: used; performed; showed; including; based; comparing; reported; identifying; presented; evaluate; detect; associated; increased; considered; require; following; found; provide; demonstrated; diagnosed; assessed; seen; obtained; confirms; predicting; suspected; propose; made; imaging; developed; learning; describe; determine; reveals; needs; underwent; training; suggested; result; improve; computed; related; classify; define; observed; reviewed; take; causes; collecting; lead adjectives: clinical; covid-19; pulmonary; high; diagnostic; normal; non; positive; different; respiratory; deep; available; pediatric; first; severe; acute; negative; early; abnormal; medical; low; significant; lower; higher; common; specific; important; pleural; new; radiological; neural; bilateral; possible; small; many; viral; initial; patient; large; potential; right; private; multiple; standard; several; novel; single; radiographic; pre; renal adverbs: also; however; well; therefore; respectively; often; especially; significantly; even; less; particularly; still; clinically; usually; highly; furthermore; mainly; finally; commonly; first; relatively; moreover; statistically; frequently; recently; potentially; currently; retrospectively; later; widely; generally; rapidly; previously; hence; least; already; almost; approximately; randomly; easily; critically; alone; specifically; similarly; mostly; early; always; typically; prior; now pronouns: we; our; it; their; they; its; them; i; she; us; he; his; her; your; one; you; themselves; itself; em; y=1then; resnet-50; ourselves; my; me; 's proper nouns: COVID-19; CXR; CT; MRI; TB; AI; US; SARS; LUS; PCR; RT; Fig; CoV-2; MR; Chest; China; Coronavirus; Imaging; Table; Health; HRCT; CNN; Radiology; DLS; H1N1; GGO; Disease; Society; LDCT; covid-19; Radiol; uenza; JIA; ChUS; RALE; CI; A; Pediatr; S.; Suppl; DWI; Covid-19; ADC; MRE; Pneumonia; Wuhan; Group; CCHF; UK; ED keywords: cxr; covid-19; lus; patient; sars; rale; image; hrct; disease; zikv; uenza; suppl; study; siamese; radiol; private; ppe; pneumonia; pediatric; pediatr; pct; pcr; objective; ntp; mri; mre; model; ldct; kenya; jia; imaging; ifd; himc; high; h1n1; ggo; finding; dwi; ds-1; dls; diagnosis; deep; datum; cxr-14; cns; cnn; child; cchf; case; adc one topic; one dimension: covid file(s): http://medrxiv.org/cgi/content/short/2020.08.19.20177907v1?rss=1 titles(s): It''s not TB but what could it be? Abnormalities on chest X-rays taken during the Kenya National Tuberculosis Prevalence Survey three topics; one dimension: covid; patients; patients file(s): https://arxiv.org/pdf/2007.02202v1.pdf, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7103096/, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7102231/ titles(s): A Survey on Applications of Artificial Intelligence in Fighting Against COVID-19 | ESPR 2017 | Influenza A virus: radiological and clinical findings of patients hospitalised for pandemic H1N1 influenza five topics; three dimensions: patients imaging mri; patients covid cxr; covid cxr images; ct patients infl; flip fourth laid file(s): https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7103096/, https://api.elsevier.com/content/article/pii/S1936878X20304770, https://arxiv.org/pdf/2007.02202v1.pdf, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7102231/, https://doi.org/10.1016/j.crad.2020.05.009 titles(s): ESPR 2017 | Heart and Lung Multimodality Imaging in COVID-19 | A Survey on Applications of Artificial Intelligence in Fighting Against COVID-19 | Influenza A virus: radiological and clinical findings of patients hospitalised for pandemic H1N1 influenza | Re: a British Society of Thoracic Imaging statement: considerations in designing local imaging diagnostic algorithms for the COVID-19 pandemic Type: cord title: keyword-cxr-cord date: 2021-05-24 time: 22:55 username: emorgan patron: Eric Morgan email: emorgan@nd.edu input: keywords:cxr ==== make-pages.sh htm files ==== make-pages.sh complex files ==== make-pages.sh named enities ==== making bibliographics id: cord-334495-7y1la856 author: Agricola, Eustachio title: Heart and Lung Multimodality Imaging in COVID-19 date: 2020-06-24 words: 6791 sentences: 325 pages: flesch: 33 cache: ./cache/cord-334495-7y1la856.txt txt: ./txt/cord-334495-7y1la856.txt summary: From a clinical point of view, cardiac involvement during COVID-19 may present a wide spectrum of severity ranging from subclinical myocardial injury to well-defined clinical entities (myocarditis, myocardial infarction, pulmonary embolism and heart failure), whose incidence and prognostic implications are currently largely unknown due to a significant lack of imaging data. The use of integrated heart and lung multimodality imaging plays a central role in different clinical settings and is essential in diagnosis, risk stratification and management of COVID-19 patients. In this context, the use of multiple diagnostic imaging techniques may apply to both heart and lung to provide an integrated assessment of cardiac and pulmonary function and to refine diagnosis, risk stratification and management of COVID-19 patients. patients not requiring ICU, when clinical presentation and biomarker alterations suggest acute-onset myocardial inflammation, if the diagnosis is likely to impact on management, CMR may be considered to confirm acute myocarditis, after exclusion of alternative relevant clinical conditions, including ACS and HF, by means of other rapidly available imaging modalities (i.e. cardiac CT scan or TTE). abstract: Abstract SARS-CoV-2 outbreak has rapidly reached a pandemic proportion and has become a major threaten to global health. Although the predominant clinical feature of COVID-19 is an acute respiratory syndrome of varying severity, ranging from mild symptomatic interstitial pneumonia to acute respiratory distress syndrome, the cardiovascular system can be involved with several facets. As many as 40% hospitalized patients presenting with COVID-19 have pre-existing history of cardiovascular disease and current estimates report a proportion of myocardial injury in COVID-19 patients ranging up to 12%. Multiple pathways have been advocated to explain this finding and the related clinical scenarios, encompassing local and systemic inflammatory response and oxygen supply-demand imbalance. From a clinical point of view, cardiac involvement during COVID-19 may present a wide spectrum of severity ranging from subclinical myocardial injury to well-defined clinical entities (myocarditis, myocardial infarction, pulmonary embolism and heart failure), whose incidence and prognostic implications are currently largely unknown due to a significant lack of imaging data. The use of integrated heart and lung multimodality imaging plays a central role in different clinical settings and is essential in diagnosis, risk stratification and management of COVID-19 patients. Aim of this review is to summarize imaging-oriented pathophysiological mechanisms of lung and cardiac involvement in COVID-19 and to provide a guide for an integrated imaging assessment in these patients. url: https://api.elsevier.com/content/article/pii/S1936878X20304770 doi: 10.1016/j.jcmg.2020.05.017 id: cord-006708-nionk55w author: Aktaş, Fatma title: The pulmonary findings of Crimean–Congo hemorrhagic fever patients with chest X-ray assessments date: 2019-03-25 words: 3583 sentences: 212 pages: flesch: 55 cache: ./cache/cord-006708-nionk55w.txt txt: ./txt/cord-006708-nionk55w.txt summary: MATERIALS AND METHODS: A total of 165 patients who were diagnosed with CCHF and examined through chest X-ray (CXR) due to respiratory symptoms at their first examination and/or during their hospitalization were included in this study. CONCLUSION: According to the results of our study, it can be suggested that radiological examination in lungs should be performed primarily with CXR and pulmonary involvement (pleural effusion and consolidation) affects survival in CCHF negatively. As a result of CXR findings obtained based on the first examination and clinical follow-up within the first 5 days, consolidation in 74 patients (44.8%), pleural effusion in 64 patients (39.8%), ground glass opacity in 49 patients (29.7%), and atelectasis in 30 patients (18.2%) were detected (Fig. 2) . In a study performed on dengue hemorrhagic fever, a total of 468 CXR taken from 363 patients were examined and parenchymal infiltration and pleural effusion were observed in more than half of the patients on the third day of follow-up. abstract: BACKGROUND: Crimean–Congo hemorrhagic fever (CCHF), characterized by fever and/or hemorrhage, is a zoonotic viral disease with high mortality. The agent causing CCHF is a Nairovirus. The virus is typically transmitted to humans through tick bites. CCHF is a life-threatening disease observed endemically over a wide geographical regions in the world, and there is limited information about pulmonary findings in CCHF patients. PURPOSE: We aimed to investigate the pulmonary findings belonging to a large CCHF patient cohort and to determine if there is any relationship between laboratory findings and disease severity. MATERIALS AND METHODS: A total of 165 patients who were diagnosed with CCHF and examined through chest X-ray (CXR) due to respiratory symptoms at their first examination and/or during their hospitalization were included in this study. In addition to demographical and laboratory findings of the patients, chest X-rays were also examined. RESULTS: Of the 165 patients examined, 96 were male (58.2%) and 69 were female (41.8%). The mean age was 51.64 ± 17.95 years (4–81 years). Single and/or multiple pathological findings were detected in 93 patients (56.4%) as a result of chest X-ray during their first examination. On chest X-ray, consolidation in 74 patients (44.8%), pleural effusion in 64 patients (39.8%), ground glass opacity in 49 patients (29.7%), and atelectasis in 30 patients (18.2%) were detected. CONCLUSION: According to the results of our study, it can be suggested that radiological examination in lungs should be performed primarily with CXR and pulmonary involvement (pleural effusion and consolidation) affects survival in CCHF negatively. url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7102267/ doi: 10.1007/s11547-019-01024-w id: cord-266672-t85wd0xq author: Bagnera, Silvia title: Performance of Radiologists in the Evaluation of the Chest Radiography with the Use of a “new software score” in Coronavirus Disease 2019 Pneumonia Suspected Patients date: 2020-07-20 words: 3053 sentences: 137 pages: flesch: 46 cache: ./cache/cord-266672-t85wd0xq.txt txt: ./txt/cord-266672-t85wd0xq.txt summary: title: Performance of Radiologists in the Evaluation of the Chest Radiography with the Use of a "new software score" in Coronavirus Disease 2019 Pneumonia Suspected Patients OBJECTIVES: The purpose of this study is to assess the performance of radiologists using a new software called "COVID-19 score" when performing chest radiography on patients potentially infected by coronavirus disease 2019 (COVID-19) pneumonia. MATERIAL AND METHODS: From February–April 2020, 14 radiologists retrospectively evaluated a pool of 312 chest X-ray exams to test a new software function for lung imaging analysis based on radiological features and graded on a three-point scale. To evaluate a new tool called "COVID-19 score" made available to radiologists for lung imaging analysis, we retrospectively included in the study patients who underwent at least two consecutive chest X-rays for a total of 312 exams. In this study, we tested a new software application called "COVID-19 score" that can be used in the reporting of chest X-ray imaging in patients suspected COVID-19, based on radiological semantic features. abstract: OBJECTIVES: The purpose of this study is to assess the performance of radiologists using a new software called “COVID-19 score” when performing chest radiography on patients potentially infected by coronavirus disease 2019 (COVID-19) pneumonia. Chest radiography (or chest X-ray, CXR) and CT are important for the imaging diagnosis of the coronavirus pneumonia (COVID-19). CXR mobile devices are efficient during epidemies, because allow to reduce the risk of contagion and are easy to sanitize. MATERIAL AND METHODS: From February–April 2020, 14 radiologists retrospectively evaluated a pool of 312 chest X-ray exams to test a new software function for lung imaging analysis based on radiological features and graded on a three-point scale. This tool automatically generates a cumulative score (0–18). The intra- rater agreement (evaluated with Fleiss’s method) and the average time for the compilation of the banner were calculated. RESULTS: Fourteen radiologists evaluated 312 chest radiographs of COVID-19 pneumonia suspected patients (80 males and 38 females) with an average age of 64, 47 years. The inter-rater agreement showed a Fleiss’ kappa value of 0.53 and the intra-group agreement varied from Fleiss’ Kappa value between 0.49 and 0.59, indicating a moderate agreement (considering as “moderate” ranges 0.4–0.6). The years of work experience were irrelevant. The average time for obtaining the result with the automatic software was between 7 s (e.g., zero COVID-19 score) and 21 s (e.g., with COVID-19 score from 6 to 12). CONCLUSION: The use of automatic software for the generation of a CXR “COVID-19 score” has proven to be simple, fast, and replicable. Implementing this tool with scores weighed on the number of lung pathological areas, a useful parameter for clinical monitoring could be available. url: https://www.ncbi.nlm.nih.gov/pubmed/32754375/ doi: 10.25259/jcis_76_2020 id: cord-310228-bqpvykce author: Borkowski, A. A. title: Using Artificial Intelligence for COVID-19 Chest X-ray Diagnosis date: 2020-05-26 words: 3193 sentences: 216 pages: flesch: 51 cache: ./cache/cord-310228-bqpvykce.txt txt: ./txt/cord-310228-bqpvykce.txt summary: We utilized publicly available CXR images for patients with COVID-19 pneumonia, pneumonia from other etiologies, and normal CXRs as a dataset to train Microsoft CustomVision. We then validated the program using CXRs of patients from our institution with confirmed COVID-19 diagnoses along with non-COVID-19 pneumonia and normal CXRs. Our model performed with 100% sensitivity, 95% specificity, 97% accuracy, 91% positive predictive value, and 100% negative predictive value. We first trained the Microsoft CustomVision automated image classification and object detection system to differentiate cases of COVID-19 from pneumonia from other etiologies as well as normal lung CXRs. We then tested our model against known patients from our medical center. We have utilized a readily available, commercial platform to demonstrate the potential of AI to assist in the successful diagnosis of COVID-19 pneumonia on CXR images. abstract: Coronavirus disease-19 (COVID-19), caused by a novel member of the coronavirus family, is a respiratory disease that rapidly reached pandemic proportions with high morbidity and mortality. It has had a dramatic impact on society and world economies in only a few months. COVID-19 presents numerous challenges to all aspects of healthcare, including reliable methods for diagnosis, treatment, and prevention. Initial efforts to contain the spread of the virus were hampered by the time required to develop reliable diagnostic methods. Artificial intelligence (AI) is a rapidly growing field of computer science with many applications to healthcare. Machine learning is a subset of AI that employs deep learning with neural network algorithms. It can recognize patterns and achieve complex computational tasks often far quicker and with increased precision than humans. In this manuscript, we explore the potential for a simple and widely available test as a chest x-ray (CXR) to be utilized with AI to diagnose COVID-19 reliably. Microsoft CustomVision is an automated image classification and object detection system that is a part of Microsoft Azure Cognitive Services. We utilized publicly available CXR images for patients with COVID-19 pneumonia, pneumonia from other etiologies, and normal CXRs as a dataset to train Microsoft CustomVision. Our trained model overall demonstrated 92.9% sensitivity (recall) and positive predictive value (precision), with results for each label showing sensitivity and positive predictive value at 94.8% and 98.9% for COVID-19 pneumonia, 89% and 91.8% for non-COVID-19 pneumonia, 95% and 88.8% for normal lung. We then validated the program using CXRs of patients from our institution with confirmed COVID-19 diagnoses along with non-COVID-19 pneumonia and normal CXRs. Our model performed with 100% sensitivity, 95% specificity, 97% accuracy, 91% positive predictive value, and 100% negative predictive value. Finally, we developed and described a publicly available website to demonstrate how this technology can be made readily available in the future. url: http://medrxiv.org/cgi/content/short/2020.05.21.20106518v1?rss=1 doi: 10.1101/2020.05.21.20106518 id: cord-167889-um3djluz author: Chen, Jianguo title: A Survey on Applications of Artificial Intelligence in Fighting Against COVID-19 date: 2020-07-04 words: 12248 sentences: 768 pages: flesch: 50 cache: ./cache/cord-167889-um3djluz.txt txt: ./txt/cord-167889-um3djluz.txt summary: The progress of CT image inspection based on AI usually includes the following steps: Region Of Interest (ROI) segmentation, lung tissue feature extraction, candidate infection region detection, and COVID-19 classification. Data sources Methods Country/region Huang [82] Yang [231] , WHO [216] CNN, LSTM, MLP, GRU China Hu [80, 81] The Paper [148] , WHO [216] MAE, clustering China Yang [233] Baidu [16] SEIR, LSTM China Fong [51, 52] NHC [139] SVM, PNN China Ai [3] WHO [54, 216] ANFIS, FPA China, USA Rizk [168] WHO [216] ISACL-MFNN USA, Italy, Spain Giuliani [62] Italy [144] EMTMGL Italy Ayyoubzadeh [14] Worldometer [218] , Google [201] LR, LSTM Iran Marini [129, 130] Swiss population Enerpol Switzerland Lai [110] IATA [126] , Worldpop [219] ML Global Punn [155] JHU CSSE [49] SVR, PR, DNN, LSTM, RNN Predicting commercially available antiviral drugs that may act on the novel coronavirus (sars-cov-2) through a drug-target interaction deep learning model abstract: The COVID-19 pandemic caused by the SARS-CoV-2 virus has spread rapidly worldwide, leading to a global outbreak. Most governments, enterprises, and scientific research institutions are participating in the COVID-19 struggle to curb the spread of the pandemic. As a powerful tool against COVID-19, artificial intelligence (AI) technologies are widely used in combating this pandemic. In this survey, we investigate the main scope and contributions of AI in combating COVID-19 from the aspects of disease detection and diagnosis, virology and pathogenesis, drug and vaccine development, and epidemic and transmission prediction. In addition, we summarize the available data and resources that can be used for AI-based COVID-19 research. Finally, the main challenges and potential directions of AI in fighting against COVID-19 are discussed. Currently, AI mainly focuses on medical image inspection, genomics, drug development, and transmission prediction, and thus AI still has great potential in this field. This survey presents medical and AI researchers with a comprehensive view of the existing and potential applications of AI technology in combating COVID-19 with the goal of inspiring researches to continue to maximize the advantages of AI and big data to fight COVID-19. url: https://arxiv.org/pdf/2007.02202v1.pdf doi: nan id: cord-292341-uo54ghf3 author: Cocconcelli, Elisabetta title: Clinical Features and Chest Imaging as Predictors of Intensity of Care in Patients with COVID-19 date: 2020-09-16 words: 5195 sentences: 249 pages: flesch: 47 cache: ./cache/cord-292341-uo54ghf3.txt txt: ./txt/cord-292341-uo54ghf3.txt summary: Univariate logistic regression analysis of factors associated with level of care revealed that sex, age, smoking history, FiO2, pO2 in room air at admission, bacterial co-infections developed during hospitalization, CVDs, metabolic and oncologic diseases and chest X-ray global score had significant positive association with a higher level of care in the entire study population (Table 3) . Univariate logistic regression analysis of factors associated with level of care revealed that sex, age, smoking history, FiO2, pO2 in room air at admission, bacterial co-infections developed during hospitalization, CVDs, metabolic and oncologic diseases and chest X-ray global score had significant positive association with a higher level of care in the entire study population (Table 3) . This is a retrospective analysis of clinical features and radiographic severity scores in patients with COVID-19 and how these parameters on hospital admission correlate with different levels of medical care (i.e., HIMC vs. abstract: Coronavirus disease 2019 (COVID-19) has rapidly become a global pandemic with lung disease representing the main cause of morbidity and mortality. Conventional chest-X ray (CXR) and ultrasound (US) are valuable instruments to assess the extent of lung involvement. We investigated the relationship between CXR scores on admission and the level of medical care required in patients with COVID-19. Further, we assessed the CXR-US correlation to explore the role of ultrasound in monitoring the course of COVID-19 pneumonia. Clinical features and CXR scores were obtained at admission and correlated with the level of intensity of care required [high- (HIMC) versus low-intensity medical care (LIMC)]. In a subgroup of patients, US findings were correlated with clinical and radiographic parameters. On hospital admission, CXR global score was higher in HIMCs compared to LIMC. Smoking history, pO(2) on admission, cardiovascular and oncologic diseases were independent predictors of HIMC. The US score was positively correlated with FiO(2) while the correlation with CXR global score only trended towards significance. Our study identifies clinical and radiographic features that strongly correlate with higher levels of medical care. The role of lung ultrasound in this setting remains undetermined and needs to be explored in larger prospective studies. url: https://www.ncbi.nlm.nih.gov/pubmed/32947904/ doi: 10.3390/jcm9092990 id: cord-006683-7rsmbk3j author: Coppola, M. title: Influenza A virus: radiological and clinical findings of patients hospitalised for pandemic H1N1 influenza date: 2011-01-12 words: 4838 sentences: 374 pages: flesch: 54 cache: ./cache/cord-006683-7rsmbk3j.txt txt: ./txt/cord-006683-7rsmbk3j.txt summary: The primary lesions assessed at CXR and chest CT were [13] and recent publications [10, 11] describing the main radiological pulmonary manifestations of A/H1N1 infl uenza: interstitial reticulation (RI; linear opacities of the central and peripheral interstitium appearing as radio-opaque lines on CXR and hyperdensities on CT), nodules (N; well-or illdefi ned, rounded opacities/hyperdensities, with maximum diameter of 3 cm.), ground-glass opacities (GGO; heterogeneous increase in parenchymal opacity with preservation of bronchial and vascular margins), consolidation (CONS; homogenously increased parenchymal attenuation that obscures the margins of the bronchial and vessels walls). To date, few studies addressing chest imaging in patients affected by infl uenza A/H1N1 have been published [10] [11] [12] , and the presentation of H1N1 virus pneumonia on both CXR and chest CT seems to refl ect the general features of viral pneumonia [23] . One study [11] reported on the main CXR and chest CT fi ndings in seven patients affected by infl uenza A/H1N1: bilateral GGO, more frequently associated with focal or multifocal areas of consolidation. abstract: PURPOSE: This paper describes the radiological and clinical findings identified in a group of patients with H1N1 influenza. MATERIALS AND METHODS: Between May and mid-November 2009, 3,649 patients with suspected H1N1 influenza presented to our hospital. Our study population comprised 167 (91 male, 76 female patients, age range 11 months to 82 years; mean age 29 years) out of 1,896 patients with throat swab positive for H1N1 and clinical and laboratory findings indicative of viral influenza. All 167 patients were studied by chest X-ray (CXR), and 20 patients with positive CXR and worsening clinical condition also underwent computed tomography (CT). The following findings were evaluated on both modalities: interstitial reticulation (IR), nodules (N), ground-glass opacities (GGO), consolidations (CONS), bacterial superinfection and pulmonary complications. RESULTS: Ninety of 167 patients had positive CXR results. Abnormalities identified on CXR, variously combined and distributed, were as follows: 53 IR, 5 N, 13 GGO, 50 CONS; the predominant combination was represented by six GGO with CONS. Of the 20 CXR-positive cases also studied by CT, 17 showed pathological findings. The abnormalities identified on CT, variously combined and distributed, were as follows: 14 IR, 2 N, 5 GGO; the predominant combination was 10 GGO with CONS. Despite the differences between the two modalities, the principle radiological findings of bacterial superinfection were tree-in-bud pattern, consolidation with air bronchogram, and pleural and pericardial effusion. Fifteen of the 20 patients studied by both CXR and chest CT showed respiratory complications with bilateral and diffuse CONS on CXR and CT. Six of 15 died: 4/6 of acute respiratory distress syndrome and 2/6 of multiple organ failure. CONCLUSIONS: Our study describes the radiological and clinical characteristics of a large population of patients affected by H1N1 influenza. CXR and chest CT identified the site and extent of the pulmonary lesions and documented signs of bacterial superinfection and pulmonary complications. url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7102231/ doi: 10.1007/s11547-011-0622-0 id: cord-337507-cqbbrnku author: Cozzi, Andrea title: Chest x-ray in the COVID-19 pandemic: Radiologists’ real-world reader performance date: 2020-09-10 words: 2594 sentences: 114 pages: flesch: 47 cache: ./cache/cord-337507-cqbbrnku.txt txt: ./txt/cord-337507-cqbbrnku.txt summary: METHODS: In this retrospective observational study we enrolled all patients presenting to the emergency department of a Milan-based university hospital from February 24th to April 8th 2020 who underwent nasopharyngeal swab for reverse transcriptase-polymerase chain reaction (RT-PCR) and anteroposterior bedside CXR within 12 h. The two largest by far are a retrospective review by a single radiologist of 518 CXRs acquired during the first phase of the pandemic peak (from March 1 st to March 15 th )with a resulting overall sensitivity of 57% [22] and a study coming from our group and performed on 535 patients [23] . Real-world data from this study, albeit conducted in a highprevalence region and during a SARS-CoV-2 pandemic peak, seem to provide a better scenario, in which radiologists with less than 10 years of experience matched the 89.0% sensitivity attained by radiologists with more than 10 years of experience, with similar disease prevalence in the CXR subsets read by each group (73% versus 77%, respectively). abstract: PURPOSE: To report real-world diagnostic performance of chest x-ray (CXR) readings during the COVID-19 pandemic. METHODS: In this retrospective observational study we enrolled all patients presenting to the emergency department of a Milan-based university hospital from February 24th to April 8th 2020 who underwent nasopharyngeal swab for reverse transcriptase-polymerase chain reaction (RT-PCR) and anteroposterior bedside CXR within 12 h. A composite reference standard combining RT-PCR results with phone-call-based anamnesis was obtained. Radiologists were grouped by CXR reading experience (Group-1, >10 years; Group-2, <10 years), diagnostic performance indexes were calculated for each radiologist and for the two groups. RESULTS: Group-1 read 435 CXRs (77.0 % disease prevalence): sensitivity was 89.0 %, specificity 66.0 %, accuracy 83.7 %. Group-2 read 100 CXRs (73.0 % prevalence): sensitivity was 89.0 %, specificity 40.7 %, accuracy 76.0 %. During the first half of the outbreak (195 CXRs, 66.7 % disease prevalence), overall sensitivity was 80.8 %, specificity 67.7 %, accuracy 76.4 %, Group-1 sensitivity being similar to Group-2 (80.6 % versus 81.5 %, respectively) but higher specificity (74.0 % versus 46.7 %) and accuracy (78.4 % versus 69.0 %). During the second half (340 CXRs, 81.8 % prevalence), overall sensitivity increased to 92.8 %, specificity dropped to 53.2 %, accuracy increased to 85.6 %, this pattern mirrored in both groups, with decreased specificity (Group-1, 58.0 %; Group-2, 33.3 %) but increased sensitivity (92.7 % and 93.5 %) and accuracy (86.5 % and 81.0 %, respectively). CONCLUSIONS: Real-world CXR diagnostic performance during the COVID-19 pandemic showed overall high sensitivity with higher specificity for more experienced radiologists. The increase in accuracy over time strengthens CXR role as a first line examination in suspected COVID-19 patients. url: https://www.ncbi.nlm.nih.gov/pubmed/32971326/ doi: 10.1016/j.ejrad.2020.109272 id: cord-331891-a6b1xanm author: Cozzi, Diletta title: Chest X-ray in new Coronavirus Disease 2019 (COVID-19) infection: findings and correlation with clinical outcome date: 2020-06-09 words: 2916 sentences: 144 pages: flesch: 47 cache: ./cache/cord-331891-a6b1xanm.txt txt: ./txt/cord-331891-a6b1xanm.txt summary: MATERIALS AND METHODS: This is a retrospective study involving patients with clinical-epidemiological suspect of COVID-19 infection, who performed CXRs at the emergency department (ED) of our University Hospital from March 1 to March 31, 2020. Radiological evaluation of patients with clinical-epidemiological suspect of COVID-19 is mandatory, especially in the emergency department (ED) while waiting for RT-PCR results, in order to have a rapid evaluation of thoracic involvement. Therefore, the purpose of our study is to better understand the main radiographic features of COVID-19 pneumonia, by describing the main CXR findings in a selected cohort of patients, also correlating the radiological appearance with RT-PCR examination and patients outcome (intended as discharged or hospitalized into a medicine department or intensive care unit). An independent and retrospective review of each CXR was performed by two thoracic radiologists in order to define the number of radiological suspects of COVID-19 infection; after this, they defined the predominant pattern of COVID-19 pneumonia presentation in patients with a positive RT-PCR. abstract: AIM: The purpose of this study is to describe the main chest radiological features (CXR) of COVID-19 and correlate them with clinical outcome. MATERIALS AND METHODS: This is a retrospective study involving patients with clinical-epidemiological suspect of COVID-19 infection, who performed CXRs at the emergency department (ED) of our University Hospital from March 1 to March 31, 2020. All patients performed RT-PCR nasopharyngeal and throat swab, CXR at the ED and clinical-epidemiological data. RT-PCR results were considered the reference standard. The final outcome was expressed as discharged or hospitalized patients into a medicine department or intensive care unit (ICU). RESULTS: Patients that had a RT-PCR positive for COVID-19 infection were 234 in total: 153 males (65.4%) and 81 females (34.6%), with a mean age of 66.04 years (range 18–97 years). Thirteen CXRs were negative for radiological thoracic involvement (5.6%). The following alterations were more commonly observed: 135 patients with lung consolidations (57.7%), 147 (62.8%) with GGO, 55 (23.5%) with nodules and 156 (66.6%) with reticular–nodular opacities. Patients with consolidations and GGO coexistent in the same radiography were 35.5% of total. Peripheral (57.7%) and lower zone distribution (58.5%) were the most common predominance. Moreover, bilateral involvement (69.2%) was most frequent than unilateral one. Baseline CXR sensitivity in our experience is about 67.1%. The most affected patients were especially males in the age group 60–79 years old (45.95%, of which 71.57% males). RALE score was slightly higher in male than in female patients. ANOVA with Games-Howell post hoc showed significant differences of RALE scores for group 1 vs 3 (p < 0.001) and 2 vs 3 (p = 0.001). Inter-reader agreement in assigning RALE score was very good (ICC: 0.92—with 95% confidence interval 0.88–0.95). CONCLUSION: In COVID-19, CXR shows patchy or diffuse reticular–nodular opacities and consolidation, with basal, peripheral and bilateral predominance. In our experience, baseline CXR had a sensitivity of 68.1%. The RALE score can be used in the emergency setting as a quantitative method of the extent of SARS-CoV-2 pneumonia, correlating with an increased risk of ICU admission. url: https://doi.org/10.1007/s11547-020-01232-9 doi: 10.1007/s11547-020-01232-9 id: cord-336843-c0sr3six author: Gerritsen, M. G. title: Improving early diagnosis of pulmonary infections in patients with febrile neutropenia using low-dose chest computed tomography date: 2017-02-24 words: 4321 sentences: 239 pages: flesch: 49 cache: ./cache/cord-336843-c0sr3six.txt txt: ./txt/cord-336843-c0sr3six.txt summary: title: Improving early diagnosis of pulmonary infections in patients with febrile neutropenia using low-dose chest computed tomography We performed a prospective study in patients with chemotherapy induced febrile neutropenia to investigate the diagnostic value of low-dose computed tomography compared to standard chest radiography. Two studies comparing LDCT to CXR in patients with persistent febrile neutropenia demonstrated an increased detection of pulmonary abnormalities. The diagnosis of possible IFD in the patient with a negative LDCT scan was based on abnormalities on HRCT made on day 4 of fever. We conducted a prospective study to evaluate whether pulmonary focus detection would improve using a LDCT scan instead of CXR on the first day of febrile neutropenia. [3] In a retrospective study 1083 adult SCT patients were evaluated, but in none of the 242 CXRs performed in asymptomatic patients with febrile neutropenia pulmonary abnormalities indicative of infection were detected. abstract: We performed a prospective study in patients with chemotherapy induced febrile neutropenia to investigate the diagnostic value of low-dose computed tomography compared to standard chest radiography. The aim was to compare both modalities for detection of pulmonary infections and to explore performance of low-dose computed tomography for early detection of invasive fungal disease. The low-dose computed tomography remained blinded during the study. A consensus diagnosis of the fever episode made by an expert panel was used as reference standard. We included 67 consecutive patients on the first day of febrile neutropenia. According to the consensus diagnosis 11 patients (16.4%) had pulmonary infections. Sensitivity, specificity, positive predictive value and negative predictive value were 36%, 93%, 50% and 88% for radiography, and 73%, 91%, 62% and 94% for low-dose computed tomography, respectively. An uncorrected McNemar showed no statistical difference (p = 0.197). Mean radiation dose for low-dose computed tomography was 0.24 mSv. Four out of 5 included patients diagnosed with invasive fungal disease had radiographic abnormalities suspect for invasive fungal disease on the low-dose computed tomography scan made on day 1 of fever, compared to none of the chest radiographs. We conclude that chest radiography has little value in the initial assessment of febrile neutropenia on day 1 for detection of pulmonary abnormalities. Low-dose computed tomography improves detection of pulmonary infiltrates and seems capable of detecting invasive fungal disease at a very early stage with a low radiation dose. url: https://doi.org/10.1371/journal.pone.0172256 doi: 10.1371/journal.pone.0172256 id: cord-296208-uy1r6lt2 author: Greenspan, Hayit title: Position paper on COVID-19 imaging and AI: from the clinical needs and technological challenges to initial AI solutions at the lab and national level towards a new era for AI in healthcare date: 2020-08-19 words: 8008 sentences: 395 pages: flesch: 47 cache: ./cache/cord-296208-uy1r6lt2.txt txt: ./txt/cord-296208-uy1r6lt2.txt summary: We focus on three specific use-cases for which AI systems can be built: early disease detection, management in a hospital setting, and building patient-specific predictive models that require the combination of imaging with additional clinical data. Many studies have emerged in the last several months from the medical imaging community with many research groups as well as companies introducing deep learning based solutions to tackle the various tasks: mostly in detection of the disease (vs normal), and more recently also for staging disease severity. In Section 2 of this paper we focus on three specific use-cases for which AI systems can be built: detection, patient management, and predictive models in which the imaging is combined with additional clinical features. Rapid ai development cycle for the coronavirus (covid-19) pandemic: Initial results for automated detection and patient monitoring using deep learning ct image analysis abstract: In this position paper, we provide a collection of views on the role of AI in the COVID-19 pandemic, from clinical requirements to the design of AI-based systems, to the translation of the developed tools to the clinic. We highlight key factors in designing system solutions - per specific task; as well as design issues in managing the disease at the national level. We focus on three specific use-cases for which AI systems can be built: early disease detection, management in a hospital setting, and building patient-specific predictive models that require the combination of imaging with additional clinical data. Infrastructure considerations and population modeling in two European countries will be described. This pandemic has made the practical and scientific challenges of making AI solutions very explicit. A discussion concludes this paper, with a list of challenges facing the community in the AI road ahead. url: https://www.ncbi.nlm.nih.gov/pubmed/32890777/ doi: 10.1016/j.media.2020.101800 id: cord-018027-goxdiyv3 author: Heussel, Claus Peter title: Diagnostic Radiology in Hematological Patients with Febrile Neutropenia date: 2014-11-27 words: 4904 sentences: 306 pages: flesch: 40 cache: ./cache/cord-018027-goxdiyv3.txt txt: ./txt/cord-018027-goxdiyv3.txt summary: Clinically, lungs are affected in 30 % of febrile neutropenic patients and allogeneic hematopoietic stem cell transplant (aSCT) recipients, paranasal sinuses in 3 % of neutropenic patients, and 30 % in the aSCT setting (concomitant to pneumonia), while the gastrointestinal tract, liver, spleen, central nervous system, and kidneys are less frequently involved [ 4 ] . While CXR provides relevant clinical information concerning central venous catheters (CVC), pleural effusion, and pulmonary congestion [ 17 ] , it fails to enable early detection or exclusion of pneumonia, which is a major task in immunocompromised hosts. For use in the context of clinical and epidemiological research in neutropenic patients, standards for the interpretation of radiological fi ndings in invasive fungal infections have been elaborated [ 10 , 51 ] ; newly emerged "typical" CT patterns (dense, well-circumscribed lesions with or without a halo sign, air-crescent sign) are classifi ed as a clinical criterion for fungal pneumonia Figs. abstract: Radiologists have a special role in the management of neutropenic patients. The appropriate investigational technique, specific differential diagnoses, and particular risks of these patients need to be understood by referring physicians as well as by radiologists. Thus, communication and cooperation, also including other clinical disciplines such as pulmonology, are required. Early detection of an infectious focus is the major goal in febrile neutropenic patients. As pneumonia is the most common focus, chest imaging is a special radiological task. The sensitivity of chest x-ray, especially in supine position, is low. Therefore, the very sensitive thin-section multislice CT became a gold standard in neutropenic hosts and might be cost effective in comparison to antibiotic treatment. CT-based localization can be used to guide invasive procedures in order to obtain samples for microbiological workup. Furthermore, the radiological characterization of infiltrates gives a first and rapid hint to discriminate between infectious (viral, typical bacterial, atypical bacterial, fungal) and noninfectious etiologies. Radiological follow-up has to take into account aspects according to disease, immune recovery, and treatment modalities. Due to a high incidence of fungal-related lung infiltrates, interpretation of follow-up findings must include further parameters besides lesion size. Apart from the lungs, also other organ systems such as the brain, liver, and paranasal sinuses need attention and are to be imaged with the appropriate technique. url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7122774/ doi: 10.1007/978-3-662-44000-1_7 id: cord-346942-88l03lf0 author: Kerpel, Ariel title: Diagnostic and Prognostic Value of Chest Radiographs for COVID-19 at Presentation date: 2020-08-17 words: 4481 sentences: 248 pages: flesch: 52 cache: ./cache/cord-346942-88l03lf0.txt txt: ./txt/cord-346942-88l03lf0.txt summary: The purpose of this study was to assess the diagnostic and prognostic value of chest radiographs (CXR) for coronavirus disease 2019 (COVID-19) at presentation. 13 Data on the strengths and weaknesses of chest radiography for the diagnosis of COVID-19 are important, as CXRs are the most commonly used triage imaging tool in any patient presenting with respiratory symptoms. We identified our study population by extracting severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RT-PCR test results (positive or negative) of nasopharyngeal swabs from all consecutive patients older than 18 years analyzed at our hospital''s laboratory from the ED from March 6-31, 2020, who had a CXR at presentation (within 24 hours of the first RT-PCR). When the RALE score was evaluated as a prognostic indicator within the COVID-19 patient group, both readers had statistically significant discriminatory accuracy for severe disease and poor outcomes (Table 3) . abstract: INTRODUCTION: Pulmonary opacities in COVID-19 increase throughout the illness and peak after ten days. The radiological literature mainly focuses on CT findings. The purpose of this study was to assess the diagnostic and prognostic value of chest radiographs (CXR) for coronavirus disease 2019 (COVID-19) at presentation. METHODS: We retrospectively identified consecutive reverse transcription polymerase reaction-confirmed COVID-19 patients (n = 104, 75% men) and patients (n = 75, 51% men) with repeated negative severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) tests. Two radiologists blindly and independently reviewed the CXRs, documented findings, assigned radiographic assessment of lung edema (RALE) scores, and predicted the patients’ COVID-19 status. We calculated interobserver reliability. The score use for diagnosis and prognosis of COVID-19 was evaluated with the area under the receiver operating characteristic curve. RESULTS: The overall RALE score failed to identify COVID-19 patients at presentation. However, the score was inversely correlated with a COVID-19 diagnosis within ≤2 days, and a positive correlation was found six days after symptom onset.Interobserver agreement with regard to separating normal from abnormal CXRs was moderate (k = 0.408) with low specificity (25% and 27%). Definite pleural effusion had almost perfect agreement (k = 0.833) and substantially reduced the odds of a COVID-19 diagnosis. Disease distribution and experts’ opinion on COVID-19 status had only fair interobserver agreement. The RALE score interobserver reliability was moderate to good (intraclass correlation coefficient = 0.745). A high RALE score predicted a poor outcome (intensive care unit hospitalization, intubation, or death) in COVID-19 patients; a score of ≥5 substantially increased the odds of having a poor outcome. CONCLUSION: Chest radiography was found not to be a valid diagnostic tool for COVID-19, as normal or near-normal CXRs are more likely early in the disease course. Pleural effusions at presentation suggest a diagnosis other than COVID-19. More extensive lung opacities at presentation are associated with poor outcome in COVID-19 patients. Thus, patients with more than minimal opacities should be monitored closely for clinical deterioration. This clinical application of CXR is its greatest strength in COVID-19 as it impacts patient care. url: https://www.ncbi.nlm.nih.gov/pubmed/32970556/ doi: 10.5811/westjem.2020.7.48842 id: cord-297198-dneycnyr author: Khan, T. title: Re: a British Society of Thoracic Imaging statement: considerations in designing local imaging diagnostic algorithms for the COVID-19 pandemic date: 2020-05-27 words: 644 sentences: 61 pages: flesch: 71 cache: ./cache/cord-297198-dneycnyr.txt txt: ./txt/cord-297198-dneycnyr.txt summary: In answering Question 2, the authors comment that "CXR may be abnormal in the majority of COVID-19 cases", incorrectly inferring that the study of Huang et al. in The Lancet does mistakenly state that they found "bilateral involvement of chest radiographs" in 40/41 patients in Table 2 of their results; however, from reading the main text, it is clear that the imaging method they are referring to is actually chest computed tomography (CT), not chest radiography (CXR): "On admission, abnormalities in chest CT images were detected among all patients. The study of Guan et al., also cited by Nair et al., reported CXR abnormalities in only 162/274 (59.1%) of COVID-19 patients. reported abnormal CXR in 44/64 (68.8%) of COVID-19 patients on presentation 4 ; however, it is important to note that both of these studies included hospitalised patients, representing individuals with more severe illness. reported CXR findings of 636 COVID-19 patients presenting to urgent-care centres and found that only 168 (26.4%) were reported originally as abnormal. abstract: nan url: https://doi.org/10.1016/j.crad.2020.05.009 doi: 10.1016/j.crad.2020.05.009 id: cord-347691-ia2i8svg author: Larici, Anna Rita title: Multimodality imaging of COVID-19 pneumonia: from diagnosis to follow-up. A comprehensive review date: 2020-08-17 words: 7456 sentences: 363 pages: flesch: 37 cache: ./cache/cord-347691-ia2i8svg.txt txt: ./txt/cord-347691-ia2i8svg.txt summary: The purpose of this comprehensive review is to understand the diagnostic capabilities and limitations of chest X-ray (CXR) and high-resolution computed tomography (HRCT) in defining the common imaging features of COVID-19 pneumonia and correlating them with the underlying pathogenic mechanisms. As suggested in the recently published WHO (World Health Organization) advice guide for the diagnosis and management of COVID-19, chest imaging should be used for diagnostic purpose in symptomatic patients if RT-PCR is not available or its results are delayed, or in case of negative result in the presence of a high clinical suspicion of COVID-19 [11] . Apart from recognizing COVID-19 pneumonia features, imaging -especially CT -may reveal possible alternative diagnoses (e.g. pulmonary oedema, alveolar haemorrhage, other type of lung infections) that justify patient''s respiratory symptoms [25, 26] . abstract: Due to its pandemic diffusion, SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus 2) infection represents a global threat. Despite a multiorgan involvement has been described, pneumonia is the most common manifestation of COVID-19 (Coronavirus disease 2019) and it is associated with a high morbidity and a considerable mortality. Especially in the areas with high disease burden, chest imaging plays a crucial role to speed up the diagnostic process and to aid the patient management. The purpose of this comprehensive review is to understand the diagnostic capabilities and limitations of chest X-ray (CXR) and high-resolution computed tomography (HRCT) in defining the common imaging features of COVID-19 pneumonia and correlating them with the underlying pathogenic mechanisms. The evolution of lung abnormalities over time, the uncommon findings, the possible complications, and the main differential diagnosis occurring in the pandemic phase of SARS-CoV-2 infection are also discussed. url: https://doi.org/10.1016/j.ejrad.2020.109217 doi: 10.1016/j.ejrad.2020.109217 id: cord-103840-2diao7zh author: Mungai, B. N. title: It''s not TB but what could it be? Abnormalities on chest X-rays taken during the Kenya National Tuberculosis Prevalence Survey date: 2020-08-22 words: 5916 sentences: 356 pages: flesch: 51 cache: ./cache/cord-103840-2diao7zh.txt txt: ./txt/cord-103840-2diao7zh.txt summary: The World Health Organization (WHO) recommends the use of chest X-ray (CXR) as a mass screening tool in TB prevalence surveys and active case finding activities to identify patients eligible for bacteriological investigation. We systematically searched MEDLINE, CINHAL, Global Health and Google scholar databases from 1940-2019 to identify studies that described the prevalence of non-TB CXR findings during TB prevalence surveys or mass screening activities. The main finding from this analysis of X-ray images from the 2016 Kenya TB prevalence survey was that the use of CXR for TB population-based studies identified a large number of patients with abnormalities, including noncommunicable diseases (NCDs) such as cardiovascular abnormalities and chronic respiratory diseases that require clinical attention. Clinically relevant cardiac and chronic pulmonary diseases accounted for 66% of the non-TB abnormalities in our setting.To our knowledge, this is the first study in sub-Saharan Africa to characterise and quantify non-TB CXR findings among participants who underwent mass screening as part of a population-based TB prevalence survey. abstract: Background: The prevalence of diseases other than tuberculosis (TB) detected during chest X-ray (CXR) screening is unknown in sub-Saharan Africa. This represents a missed opportunity for identification and treatment of potentially significant disease. Our aim was to quantify and characterise non-TB abnormalities identified by TB-focused CXR screening during the 2016 Kenya National TB prevalence survey. Methods: We reviewed a random sample of 1140 adult ([≥]15 years) CXRs classified as "abnormal, suggestive of TB" or "abnormal other" during field interpretation from the TB Prevalence Survey. Each image was read (blinded to field classification and study radiologist read) by two expert radiologists, with images classified into one of four major anatomical categories and primary radiological diagnosis. A third reader resolved discrepancies. Prevalence and 95% confidence intervals of abnormalities diagnosis were estimated. Findings: Cardiomegaly was the most common non-TB abnormality at 259/1123 (23*1%, 95% CI 20*6%-25*6%), while cardiomegaly with features of cardiac failure occurred in 17/1123 (1*5 %, 95% CI 0.9%-2*4%). We also identified chronic pulmonary pathology including suspected chronic obstructive pulmonary disease in 3*2% (95% CI 2*3%- 4*4%) and non-specific patterns in 4*6% (95% CI 3*5%- 6*0%). Prevalence of active-TB and severe post-TB lung changes was 3*6% (95% CI 2*6%- 4*8%) and 1*4% (95% CI 0*8%- 2*3%) respectively. Interpretation: Based on radiological diagnosis, we identified a wide variety of non-TB diagnoses during population-based TB screening. TB prevalence surveys and active case finding activities using mass CXR offer an opportunity to integrate disease screening efforts. Funding National Institute for Health Research (IMPALA-grant reference 16/136/35). url: http://medrxiv.org/cgi/content/short/2020.08.19.20177907v1?rss=1 doi: 10.1101/2020.08.19.20177907 id: cord-157444-huvnyali author: Nabulsi, Zaid title: Deep Learning for Distinguishing Normal versus Abnormal Chest Radiographs and Generalization to Unseen Diseases date: 2020-10-22 words: 6802 sentences: 326 pages: flesch: 44 cache: ./cache/cord-157444-huvnyali.txt txt: ./txt/cord-157444-huvnyali.txt summary: In this work, we evaluated the DLS''s performance on 6 independent test sets consisting of different patient populations, spanning three countries, and with two unseen diseases (TB and COVID-19). However, as other acute diseases may share a similar clinical presentation, many cases negative for COVID-19 exhibited abnormal CXR findings that likely triggered the DLS ( Figure 5, Supplementary Figure 5 ). Finally, to facilitate the continued development of AI models for chest radiography, we are releasing our abnormal versus normal labels from 3 radiologists (2430 labels on 810 images) for the publicly-available CXR-14 test set. Two datasets were used to evaluate the DLS''s performance in distinguishing normal and abnormal findings in a general abnormality detection setting. To compare the DLS with radiologists in classifying CXRs as normal versus abnormal, additional radiologists reviewed all test images without referencing additional clinical or patient data. abstract: Chest radiography (CXR) is the most widely-used thoracic clinical imaging modality and is crucial for guiding the management of cardiothoracic conditions. The detection of specific CXR findings has been the main focus of several artificial intelligence (AI) systems. However, the wide range of possible CXR abnormalities makes it impractical to build specific systems to detect every possible condition. In this work, we developed and evaluated an AI system to classify CXRs as normal or abnormal. For development, we used a de-identified dataset of 248,445 patients from a multi-city hospital network in India. To assess generalizability, we evaluated our system using 6 international datasets from India, China, and the United States. Of these datasets, 4 focused on diseases that the AI was not trained to detect: 2 datasets with tuberculosis and 2 datasets with coronavirus disease 2019. Our results suggest that the AI system generalizes to new patient populations and abnormalities. In a simulated workflow where the AI system prioritized abnormal cases, the turnaround time for abnormal cases reduced by 7-28%. These results represent an important step towards evaluating whether AI can be safely used to flag cases in a general setting where previously unseen abnormalities exist. url: https://arxiv.org/pdf/2010.11375v1.pdf doi: nan id: cord-297396-r1p7xn3a author: Ng, Ming-Yen title: Development and Validation of Risk Prediction Models for COVID-19 Positivity in a Hospital Setting date: 2020-09-15 words: 3251 sentences: 182 pages: flesch: 53 cache: ./cache/cord-297396-r1p7xn3a.txt txt: ./txt/cord-297396-r1p7xn3a.txt summary: OBJECTIVES: To develop:(1) two validated risk prediction models for COVID-19 positivity using readily available parameters in a general hospital setting; (2) nomograms and probabilities to allow clinical utilisation.  Developed two simple-to use nomograms for identifying COVID-19 positive patients  Probabilities are provided to allow healthcare leaders to decide suitable cut-offs  Variables are age, white cell count, chest x-ray appearances and contact history  Model variables are easily available in the general hospital setting. To develop: (1) two validated risk prediction models for COVID-19 positivity using readily available parameters in a general hospital setting; (2) nomograms and probabilities to allow clinical utilisation. Thus, a COVID-19 prediction model based on clinical, laboratory and radiological findings which presents the sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) would allow public healthcare systems to decide a suitable strategy on prioritizing tests when such RT-PCR availability is constrained. abstract: OBJECTIVES: To develop:(1) two validated risk prediction models for COVID-19 positivity using readily available parameters in a general hospital setting; (2) nomograms and probabilities to allow clinical utilisation. METHODS: Patients with and without COVID-19 were included from 4 Hong Kong hospitals. Database was randomly split 2:1 for model development database (n = 895) and validation database (n = 435). Multivariable logistic regression was utilised for model creation and validated with the Hosmer-Lemeshow (H-L) test and calibration plot. Nomograms and probabilities set at 0.1, 0.2, 0.4, 0.6 were calculated to determine sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV). RESULTS: 1330 patients (mean age 58.2 ± 24.5 years; 50.7% males; 296 COVID-19 positive) were recruited. First prediction model developed had age, total white blood cell count, chest x-ray appearances and contact history as significant predictors (AUC = 0.911 [CI = 0.880-0.941]). Second model developed has same variables except contact history (AUC = 0.880 [CI = 0.844-0.916]). Both were externally validated on H-L test (p = 0.781 and 0.155 respectively) and calibration plot. Models were converted to nomograms. Lower probabilities give higher sensitivity and NPV; higher probabilities give higher specificity and PPV. CONCLUSION: Two simple-to-use validated nomograms were developed with excellent AUCs based on readily available parameters and can be considered for clinical utilisation. url: https://api.elsevier.com/content/article/pii/S1201971220307384 doi: 10.1016/j.ijid.2020.09.022 id: cord-028786-400vglzm author: Oloko-Oba, Mustapha title: Diagnosing Tuberculosis Using Deep Convolutional Neural Network date: 2020-06-05 words: 2438 sentences: 118 pages: flesch: 44 cache: ./cache/cord-028786-400vglzm.txt txt: ./txt/cord-028786-400vglzm.txt summary: We propose a Computer-Aided Detection model using Deep Convolutional Neural Networks to automatically detect TB from Montgomery County (MC) Tuberculosis radiographs. As a result, to profer solution to the issue of limited or lack of expert radiologist and misdiagnosis of CXR, we propose a Deep Convolutional Neural Networks (CNN) model that will automatically diagnose large numbers of CXR at a time for TB manifestation in developing regions where TB is most prevalent. A model based on Deep Convolutional Neural Network (CNN) structure has been proposed in this work for the detection and classification of Tuberculosis. Presented in this paper is a model that aids early detection of Tuberculosis using CNN structure to automatically extract distinctive features from chest radiographs and classify them into normal and abnormal categories. TX-CNN: detecting tuberculosis in chest X-ray images using convolutional neural network abstract: One of the global topmost causes of death is Tuberculosis (TB) which is caused by mycobacterium bacillus. The increase rate of infected people and the recorded deaths from TB disease is as a result of its transmissibility, lack of early diagnosis, and inadequate professional radiologist in developing regions where TB is more prevalent. Tuberculosis is unquestionably curable but needs to be detected early for necessary treatment to be effective. Many screening techniques are available, but chest radiograph has proven to be valuable for screening pulmonary diseases but hugely dependent on the interpretational skill of an expert radiologist. We propose a Computer-Aided Detection model using Deep Convolutional Neural Networks to automatically detect TB from Montgomery County (MC) Tuberculosis radiographs. Our proposed model performed at 87.1% validation accuracy and evaluated using confusion matrix and accuracy as metrics. url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7340926/ doi: 10.1007/978-3-030-51935-3_16 id: cord-282198-ugmv9om1 author: Pare, Joseph R. title: Point-of-care Lung Ultrasound Is More Sensitive than Chest Radiograph for Evaluation of COVID-19 date: 2020-06-19 words: 3406 sentences: 193 pages: flesch: 51 cache: ./cache/cord-282198-ugmv9om1.txt txt: ./txt/cord-282198-ugmv9om1.txt summary: Our primary objective was to determine whether lung ultrasound (LUS) B-lines, when excluding patients with alternative etiologies for B-lines, are more sensitive for the associated diagnosis of COVID-19 than CXR. METHODS: This was a retrospective cohort study of all patients who presented to a single, academic emergency department in the United States between March 20 and April 6, 2020, and received LUS, CXR, and viral testing for COVID-19 as part of their diagnostic evaluation. Lung ultrasound (LUS) has been shown to outperform chest radiograph (CXR) in its ability to detect abnormalities with non-coronavirus disease 2019 (COVID-19) pulmonary infections. This was a retrospective, observational, cohort study of patients undergoing COVID-19 testing (based on real-time reverse transcriptase-polymerase chain reaction [RT-PCR] of nasopharyngeal sampling performed on an assay developed by the Center for Regenerative Medicine at Boston University, operating under an Emergency Use Authorization], who also had both diagnostic LUS and CXR for the evaluation of COVID-19 in the emergency department (ED). abstract: INTRODUCTION: Current recommendations for diagnostic imaging for moderately to severely ill patients with suspected coronavirus disease 2019 (COVID-19) include chest radiograph (CXR). Our primary objective was to determine whether lung ultrasound (LUS) B-lines, when excluding patients with alternative etiologies for B-lines, are more sensitive for the associated diagnosis of COVID-19 than CXR. METHODS: This was a retrospective cohort study of all patients who presented to a single, academic emergency department in the United States between March 20 and April 6, 2020, and received LUS, CXR, and viral testing for COVID-19 as part of their diagnostic evaluation. The primary objective was to estimate the test characteristics of both LUS B-lines and CXR for the associated diagnosis of COVID-19. Our secondary objective was to evaluate the proportion of patients with COVID-19 that have secondary LUS findings of pleural abnormalities and subpleural consolidations. RESULTS: We identified 43 patients who underwent both LUS and CXR and were tested for COVID-19. Of these, 27/43 (63%) tested positive. LUS was more sensitive (88.9%, 95% confidence interval (CI), 71.1–97.0) for the associated diagnosis of COVID-19 than CXR (51.9%, 95% CI, 34.0–69.3; p = 0.013). LUS and CXR specificity were 56.3% (95% CI, 33.2–76.9) and 75.0% (95% CI, 50.0–90.3), respectively (p = 0.453). Secondary LUS findings of patients with COVID-19 demonstrated 21/27 (77.8%) had pleural abnormalities and 10/27 (37%) had subpleural consolidations. CONCLUSION: Among patients who underwent LUS and CXR, LUS was found to have a higher sensitivity than CXR for the evaluation of COVID-19. This data could have important implications as an aid in the diagnostic evaluation of COVID-19, particularly where viral testing is not available or restricted. If generalizable, future directions would include defining how to incorporate LUS into clinical management and its role in screening lower-risk populations. url: https://www.ncbi.nlm.nih.gov/pubmed/32726240/ doi: 10.5811/westjem.2020.5.47743 id: cord-355218-eici4eit author: Punn, Narinder Singh title: Automated diagnosis of COVID-19 with limited posteroanterior chest X-ray images using fine-tuned deep neural networks date: 2020-10-17 words: 5950 sentences: 324 pages: flesch: 48 cache: ./cache/cord-355218-eici4eit.txt txt: ./txt/cord-355218-eici4eit.txt summary: Recently, with the release of publicly available datasets of corona positive patients comprising of computed tomography (CT) and chest X-ray (CXR) imaging; scientists, researchers and healthcare experts are contributing for faster and automated diagnosis of COVID-19 by identifying pulmonary infections using deep learning approaches to achieve better cure and treatment. Following from this context, this article presents the random oversampling and weighted class loss function approach for unbiased fine-tuned learning (transfer learning) in various state-of-the-art deep learning approaches such as baseline ResNet, Inception-v3, Inception ResNet-v2, DenseNet169, and NASNetLarge to perform binary classification (as normal and COVID-19 cases) and also multi-class classification (as COVID-19, pneumonia, and normal case) of posteroanterior CXR images. [31] proposed a deep convolutional neural network based automatic prediction model of COVID-19 with the help of pre-trained transfer models using CXR images. Detection of coronavirus (covid-19) associated pneumonia based on generative adversarial networks and a fine-tuned deep transfer learning model using chest x-ray dataset abstract: The novel coronavirus 2019 (COVID-19) is a respiratory syndrome that resembles pneumonia. The current diagnostic procedure of COVID-19 follows reverse-transcriptase polymerase chain reaction (RT-PCR) based approach which however is less sensitive to identify the virus at the initial stage. Hence, a more robust and alternate diagnosis technique is desirable. Recently, with the release of publicly available datasets of corona positive patients comprising of computed tomography (CT) and chest X-ray (CXR) imaging; scientists, researchers and healthcare experts are contributing for faster and automated diagnosis of COVID-19 by identifying pulmonary infections using deep learning approaches to achieve better cure and treatment. These datasets have limited samples concerned with the positive COVID-19 cases, which raise the challenge for unbiased learning. Following from this context, this article presents the random oversampling and weighted class loss function approach for unbiased fine-tuned learning (transfer learning) in various state-of-the-art deep learning approaches such as baseline ResNet, Inception-v3, Inception ResNet-v2, DenseNet169, and NASNetLarge to perform binary classification (as normal and COVID-19 cases) and also multi-class classification (as COVID-19, pneumonia, and normal case) of posteroanterior CXR images. Accuracy, precision, recall, loss, and area under the curve (AUC) are utilized to evaluate the performance of the models. Considering the experimental results, the performance of each model is scenario dependent; however, NASNetLarge displayed better scores in contrast to other architectures, which is further compared with other recently proposed approaches. This article also added the visual explanation to illustrate the basis of model classification and perception of COVID-19 in CXR images. url: https://arxiv.org/pdf/2004.11676v5.pdf doi: 10.1007/s10489-020-01900-3 id: cord-127759-wpqdtdjs author: Qi, Xiao title: Chest X-ray Image Phase Features for Improved Diagnosis of COVID-19 Using Convolutional Neural Network date: 2020-11-06 words: 3896 sentences: 250 pages: flesch: 50 cache: ./cache/cord-127759-wpqdtdjs.txt txt: ./txt/cord-127759-wpqdtdjs.txt summary: In this study, we design a novel multi-feature convolutional neural network (CNN) architecture for multi-class improved classification of COVID-19 from CXR images. In this work we show how local phase CXR features based image enhancement improves the accuracy of CNN architectures for COVID-19 diagnosis. Our proposed method is designed for processing CXR images and consists of two main stages as illustrated in Figure 1 : 1-We enhance the CXR images (CXR(x, y)) using local phase-based image processing method in order to obtain a multi-feature CXR image (M F (x, y)), and 2-we classify CXR(x, y) by designing a deep learning approach where multi feature CXR images (M F (x, y)), together with original CXR data (CXR(x, y)), is used for improving the classification performance. Our proposed multi-feature CNN architectures were trained on a large dataset in terms of the number of COVID-19 CXR scans and have achieved improved classification accuracy across all classes. abstract: Recently, the outbreak of the novel Coronavirus disease 2019 (COVID-19) pandemic has seriously endangered human health and life. Due to limited availability of test kits, the need for auxiliary diagnostic approach has increased. Recent research has shown radiography of COVID-19 patient, such as CT and X-ray, contains salient information about the COVID-19 virus and could be used as an alternative diagnosis method. Chest X-ray (CXR) due to its faster imaging time, wide availability, low cost and portability gains much attention and becomes very promising. Computational methods with high accuracy and robustness are required for rapid triaging of patients and aiding radiologist in the interpretation of the collected data. In this study, we design a novel multi-feature convolutional neural network (CNN) architecture for multi-class improved classification of COVID-19 from CXR images. CXR images are enhanced using a local phase-based image enhancement method. The enhanced images, together with the original CXR data, are used as an input to our proposed CNN architecture. Using ablation studies, we show the effectiveness of the enhanced images in improving the diagnostic accuracy. We provide quantitative evaluation on two datasets and qualitative results for visual inspection. Quantitative evaluation is performed on data consisting of 8,851 normal (healthy), 6,045 pneumonia, and 3,323 Covid-19 CXR scans. In Dataset-1, our model achieves 95.57% average accuracy for a three classes classification, 99% precision, recall, and F1-scores for COVID-19 cases. For Dataset-2, we have obtained 94.44% average accuracy, and 95% precision, recall, and F1-scores for detection of COVID-19. Conclusions: Our proposed multi-feature guided CNN achieves improved results compared to single-feature CNN proving the importance of the local phase-based CXR image enhancement. url: https://arxiv.org/pdf/2011.03585v1.pdf doi: nan id: cord-013065-oj0wsstz author: Rodríguez-Fanjul, Javier title: Procalcitonin and lung ultrasound algorithm to diagnose severe pneumonia in critical paediatric patients (PROLUSP study). A randomised clinical trial date: 2020-10-08 words: 3735 sentences: 225 pages: flesch: 44 cache: ./cache/cord-013065-oj0wsstz.txt txt: ./txt/cord-013065-oj0wsstz.txt summary: title: Procalcitonin and lung ultrasound algorithm to diagnose severe pneumonia in critical paediatric patients (PROLUSP study). Besides this, the use of biomarkers such as procalcitonin (PCT) has become more widespread during the past 10 years, helping clinicians diagnose bacterial etiology, especially in patients who have only had a fever for a few hours or those admitted to intensive care units [16] [17] [18] [19] [20] . Therefore, we propose this clinical trial, based on combining LUS and PCT in an algorithm with the aim to improve quality of care in children with pneumonia in a PICU. The use procalcitonin and lung ultrasound algorithm will help us diagnose bacterial pneumonia accurately and prescribe the correct antibiotic treatment. This clinical trial is focused on improving the quality of care for paediatric patients with suspected bacterial pneumonia. This clinical trial is focused on improving the quality of care for paediatric patients with suspected bacterial pneumonia. abstract: BACKGROUND: Lung ultrasound (LUS) in combination with a biomarker has not yet been studied. We propose a clinical trial where the primary aims are: 1. To assess whether an algorithm with LUS and procalcitonin (PCT) may be useful for diagnosing bacterial pneumonia; 2. To analyse the sensitivity and specificity of LUS vs chest X-ray (CXR). METHODS/DESIGN: A 3-year clinical trial. Inclusion criteria: children younger than 18 years old with suspected pneumonia in a Paediatric Intensive Care Unit. Patients will be randomised into two groups: Experimental Group: LUS will be performed as first lung image. Control Group: CXR will be performed as first pulmonary image. Patients will be classified according to the image and the PCT: a) PCT < 1 ng/mL and LUS/CXR are not suggestive of bacterial pneumonia (BN), no antibiotic will be prescribed; b) LUS/CXR are suggestive of BN, regardless of the PCT, antibiotic therapy is recommended; c) LUS/CXR is not suggestive of BN and PCT > 1 ng/mL, antibiotic therapy is recommended. CONCLUSION: This algorithm will help us to diagnose bacterial pneumonia and to prescribe the correct antibiotic treatment. A reduction of antibiotics per patient, of the treatment length, and of the exposure to ionizing radiation and in costs is expected. TRIAL REGISTRATION: NCT04217980. url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7543673/ doi: 10.1186/s12931-020-01476-z id: cord-303483-wendrxee author: Rubin, Geoffrey D. title: The Role of Chest Imaging in Patient Management during the COVID-19 Pandemic: A Multinational Consensus Statement from the Fleischner Society date: 2020-04-07 words: 4315 sentences: 189 pages: flesch: 37 cache: ./cache/cord-303483-wendrxee.txt txt: ./txt/cord-303483-wendrxee.txt summary: Thoracic imaging with chest radiography (CXR) and computed tomography (CT) are key tools for pulmonary disease diagnosis and management, but their role in the management of COVID-19 has not been considered within the multivariable context of the severity of respiratory disease, pre-test probability, risk factors for disease progression, and critical resource constraints. Thoracic imaging with chest radiography (CXR) and computed tomography (CT) are key tools for pulmonary disease diagnosis and management, but their role in the management of COVID-19 has not been considered within the multivariable context of the severity of respiratory disease, pre-test probability, risk factors for disease progression, and critical resource constraints. The severity of respiratory disease and pre-test probability of COVID-19 infection are specified for each scenario, with additional key considerations including the presence of risk factors for disease progression, evidence of disease progression, and the presence of significant critical resource constraints ( Table 1) . abstract: With more than 900,000 confirmed cases worldwide and nearly 50,000 deaths during the first three months of 2020, the COVID-19 pandemic has emerged as an unprecedented healthcare crisis. The spread of COVID-19 has been heterogeneous, resulting in some regions having sporadic transmission and relatively few hospitalized patients with COVID-19 and others having community transmission that has led to overwhelming numbers of severe cases. For these regions, healthcare delivery has been disrupted and compromised by critical resource constraints in diagnostic testing, hospital beds, ventilators, and healthcare workers who have fallen ill to the virus exacerbated by shortages of personal protective equipment. While mild cases mimic common upper respiratory viral infections, respiratory dysfunction becomes the principal source of morbidity and mortality as the disease advances. Thoracic imaging with chest radiography (CXR) and computed tomography (CT) are key tools for pulmonary disease diagnosis and management, but their role in the management of COVID-19 has not been considered within the multivariable context of the severity of respiratory disease, pre-test probability, risk factors for disease progression, and critical resource constraints. To address this deficit, a multidisciplinary panel comprised principally of radiologists and pulmonologists from 10 countries with experience managing COVID-19 patients across a spectrum of healthcare environments evaluated the utility of imaging within three scenarios representing varying risk factors, community conditions, and resource constraints. Fourteen key questions, corresponding to 11 decision points within the three scenarios and three additional clinical situations, were rated by the panel based upon the anticipated value of the information that thoracic imaging would be expected to provide. The results were aggregated, resulting in five main and three additional recommendations intended to guide medical practitioners in the use of CXR and CT in the management of COVID-19. url: https://doi.org/10.1148/radiol.2020201365 doi: 10.1148/radiol.2020201365 id: cord-275974-uqd30v7b author: Shorfuzzaman, Mohammad title: MetaCOVID: A Siamese neural network framework with contrastive loss for n-shot diagnosis of COVID-19 patients date: 2020-10-17 words: 5429 sentences: 268 pages: flesch: 49 cache: ./cache/cord-275974-uqd30v7b.txt txt: ./txt/cord-275974-uqd30v7b.txt summary: In summary, following are the contributions of our work: (a) A meta learning framework called MetaCOVID based on Siamese neural network is presented for diagnosis of COVID-19 patients from chest X-ray images, (b) The proposed work focuses on the benefit of using contrastive loss and n-shot learning in framework design, (c) A fine-tuned pre-trained VGG encoder is used to capture unbiased feature representations to improve feature embeddings from the input images, (d) The COVID-19 diagnosis problem is formulated as a k-way, n-shot classification problem where k and n represent the number of class labels and data samples used for model training, (e) Performance evaluation is presented to demonstrate the efficacy of the proposed framework with a limited dataset. In contrast, we have proposed an end-to-end trainable nshot deep meta learning framework based on Siamese neural network to classify COVID-19 cases with limited training CXR images. abstract: Various AI functionalities such as pattern recognition and prediction can effectively be used to diagnose (recognize) and predict coronavirus disease 2019 (COVID-19) infections and propose timely response (remedial action) to minimize the spread and impact of the virus. Motivated by this, an AI system based on deep meta learning has been proposed in this research to accelerate analysis of chest X-ray (CXR) images in automatic detection of COVID-19 cases. We present a synergistic approach to integrate contrastive learning with a fine-tuned pre-trained ConvNet encoder to capture unbiased feature representations and leverage a Siamese network for final classification of COVID-19 cases. We validate the effectiveness of our proposed model using two publicly available datasets comprising images from normal, COVID-19 and other pneumonia infected categories. Our model achieves 95.6% accuracy and AUC of 0.97 in diagnosing COVID-19 from CXR images even with a limited number of training samples. url: https://www.ncbi.nlm.nih.gov/pubmed/33100403/ doi: 10.1016/j.patcog.2020.107700 id: cord-350636-ufwfitue author: Shumilov, Evgenii title: Comparison of Chest Ultrasound and Standard X-Ray Imaging in COVID-19 Patients date: 2020-09-02 words: 2368 sentences: 137 pages: flesch: 49 cache: ./cache/cord-350636-ufwfitue.txt txt: ./txt/cord-350636-ufwfitue.txt summary: We aimed to investigate patterns of ChUS in COVID-19 patients and compare the findings with results from chest X-ray (CRX). We aimed to investigate patterns of ChUS in COVID-19 patients and compare the findings with results from chest X-ray (CRX). Besides pathological B-lines and subpleural consolidations, pleural line abnormality (89 %; n = 16/18) was the third most common feature in patients with respiratory manifestations of COVID-19 detected by ChUS. Besides pathological B-lines and subpleural consolidations, pleural line abnormality (89 %; n = 16/18) was the third most common feature in patients with respiratory manifestations of COVID-19 detected by ChUS. A Clinical Study of Noninvasive Assessment of Lung Lesions in Patients with Coronavirus Disease-19 (COVID-19) by Bedside Ultrasound abstract: PURPOSE: The COVID-19 pandemic poses new challenges for the medical community due to its large number of patients presenting with varying symptoms. Chest ultrasound (ChUS) may be particularly useful in the early clinical management in suspected COVID-19 patients due to its broad availability and rapid application. We aimed to investigate patterns of ChUS in COVID-19 patients and compare the findings with results from chest X-ray (CRX). MATERIALS AND METHODS: 24 patients (18 symptomatic, 6 asymptomatic) with confirmed SARS-CoV-2 by polymerase chain reaction underwent bedside ChUS in addition to CRX following admission. Subsequently, the results of ChUS and CRX were compared. RESULTS: 94% (n=17/18) of patients with respiratory symptoms demonstrated lung abnormalities on ChUS. ChUS was especially useful to detect interstitial syndrome compared to CXR in COVID-19 patients (17/18 vs. 11/18; p<0.02). Of note, ChUS also detected lung consolidations very effectively (14/18 for ChUS vs. 7/18 cases for CXR; p<0.02). Besides pathological B-lines and subpleural consolidations, pleural line abnormality (89%; n=16/18) was the third most common feature in patients with respiratory manifestations of COVID-19 detected by ChUS. CONCLUSION: Our findings support the high value of ChUS in the management of COVID-19 patients. url: https://www.ncbi.nlm.nih.gov/pubmed/32905446/ doi: 10.1055/a-1217-1603 id: cord-294557-4h0sybiy author: Stogiannos, N. title: Coronavirus disease 2019 (COVID-19) in the radiology department: What radiographers need to know date: 2020-06-04 words: 6725 sentences: 377 pages: flesch: 50 cache: ./cache/cord-294557-4h0sybiy.txt txt: ./txt/cord-294557-4h0sybiy.txt summary: Objectives include to: i) outline pathophysiology and basic epidemiology useful for radiographers, ii) discuss the role of medical imaging in the diagnosis of Covid-19, iii) summarise national and international guidelines of imaging Covid-19, iv) present main clinical and imaging findings and v) summarise current safety recommendations for medical imaging practice. CXR imaging of suspected or confirmed Covid-19 cases should be performed with portable equipment within specifically designated isolated rooms for eliminating the risks of cross-infection within the Radiology department. After the outbreak of the Covid-19 pandemic, many professional bodies and learned societies have been quick to issue official guidelines on how medical imaging should optimally be performed for early diagnosis and related management of these patients, but also how staff should be protected from cross-infection. Chest radiographic and CT findings of the 2019 novel Coronavirus disease (COVID-19): analysis of nine patients treated in Korea Imaging and clinical features of patients with 2019 novel coronavirus SARS-CoV-2: a systematic review and meta-analysis abstract: OBJECTIVES: The aim is to review current literature related to the diagnosis, management, and follow-up of suspected and confirmed Covid-19 cases. KEY FINDINGS: Medical Imaging plays an important auxiliary role in the diagnosis of Covid-19 patients, mainly those most seriously affected. Practice differs widely among different countries, mainly due to the variability of access to resources (viral testing and imaging equipment, specialised staff, protective equipment). It has been now well-documented that chest radiographs should be the first-line imaging tool and chest CT should only be reserved for critically ill patients, or when chest radiograph and clinical presentation may be inconclusive. CONCLUSION: As radiographers work on the frontline, they should be aware of the potential risks associated with Covid-19 and engage in optimal strategies to reduce these. Their role in vetting, conducting and often reporting the imaging examinations is vital as well as their contribution in patient safety and care. Medical Imaging should be limited to critically ill patients, and where it may have an impact on the patient management plan. IMPLICATIONS FOR PRACTICE: At the time of publication, this review offers the most up-to-date recommendations for clinical practitioners in radiology departments, including radiographers. Radiography practice has to significantly adjust to these new requirements to support optimal and safe imaging practices for the diagnosis of Covid-19. The adoption of low dose CT, rigorous infection control protocols and optimal use of personal protective equipment may reduce the potential risks of radiation exposure and infection, respectively, within Radiology departments. url: https://www.sciencedirect.com/science/article/pii/S1078817420300845 doi: 10.1016/j.radi.2020.05.012 id: cord-312251-t6omrr07 author: Vancheri, Sergio Giuseppe title: Radiographic findings in 240 patients with COVID-19 pneumonia: time-dependence after the onset of symptoms date: 2020-05-30 words: 3502 sentences: 189 pages: flesch: 48 cache: ./cache/cord-312251-t6omrr07.txt txt: ./txt/cord-312251-t6omrr07.txt summary: OBJECTIVE: To analyze the most frequent radiographic features of COVID-19 pneumonia and assess the effectiveness of chest X-ray (CXR) in detecting pulmonary alterations. Alteration''s type (reticular/ground-glass opacity (GGO)/consolidation) and distribution (bilateral/unilateral, upper/middle/lower fields, peripheral/central) were noted. CONCLUSIONS: The most frequent lesions in COVID-19 patients were GGO (intermediate/late phase) and reticular alteration (early phase) while consolidation gradually increased over time. Our study aimed to evaluate the percentage of abnormal chest radiographs at different time intervals from the onset of symptoms and to identify the type and distribution of radiographic alterations and their frequency at different times throughout the disease course of COVID-19 pneumonia. Chest CT showed high sensitivity in detecting GGO, which is considered a typical finding in COVID-19 pneumonia and, in some cases, may be the only alteration present in the early phases of the disease [3, 16] . abstract: OBJECTIVE: To analyze the most frequent radiographic features of COVID-19 pneumonia and assess the effectiveness of chest X-ray (CXR) in detecting pulmonary alterations. MATERIALS AND METHODS: CXR of 240 symptomatic patients (70% male, mean age 65 ± 16 years), with SARS-CoV-2 infection confirmed by RT-PCR, was retrospectively evaluated. Patients were clustered in four groups based on the number of days between symptom onset and CXR: group A (0–2 days), 49 patients; group B (3–5), 75 patients; group C (6–9), 85 patients; and group D (> 9), 31 patients. Alteration’s type (reticular/ground-glass opacity (GGO)/consolidation) and distribution (bilateral/unilateral, upper/middle/lower fields, peripheral/central) were noted. Statistical significance was tested using chi-square test. RESULTS: Among 240 patients who underwent CXR, 180 (75%) showed alterations (group A, 63.3%; group B, 72%; group C, 81.2%; group D, 83.9%). GGO was observed in 124/180 patients (68.8%), reticular alteration in 113/180 (62.7%), and consolidation in 71/180 (39.4%). Consolidation was significantly less frequent (p < 0.01). Distribution among groups was as follows: reticular alteration (group A, 70.9%; group B, 72.2%; group C, 57.9%; group D, 46.1%), GGO (group A, 67.7%; group B, 62.9%; group C, 71%; group D, 76.9%), and consolidation (group A, 35.5%; group B, 31.4%; group C, 47.8%; group D, 38.5%). Alterations were bilateral in 73.3%. Upper, middle, and lower fields were involved in 36.7%, 79.4%, and 87.8%, respectively. Lesions were peripheral in 49.4%, central in 11.1%, or both in 39.4%. Upper fields and central zones were significantly less involved (p < 0.01). CONCLUSIONS: The most frequent lesions in COVID-19 patients were GGO (intermediate/late phase) and reticular alteration (early phase) while consolidation gradually increased over time. The most frequent distribution was bilateral, peripheral, and with middle/lower predominance. Overall rate of negative CXR was 25%, which progressively decreased over time. KEY POINTS: • The predominant lung changes were GGO and reticular alteration, while consolidation was less frequent. • The typical distribution pattern was bilateral, peripheral, or both peripheral and central and involved predominantly the lower and middle fields. • Chest radiography showed lung abnormalities in 75% of patients with confirmed SARS-CoV-2 infection, range varied from 63.3 to 83.9%, respectively, at 0–2 days and > 9 days from the onset of symptoms. url: https://www.ncbi.nlm.nih.gov/pubmed/32474630/ doi: 10.1007/s00330-020-06967-7 id: cord-269014-ck27fm58 author: Vo, Luan Nguyen Quang title: Enhanced Private Sector Engagement for Tuberculosis Diagnosis and Reporting through an Intermediary Agency in Ho Chi Minh City, Viet Nam date: 2020-09-14 words: 5040 sentences: 238 pages: flesch: 49 cache: ./cache/cord-269014-ck27fm58.txt txt: ./txt/cord-269014-ck27fm58.txt summary: title: Enhanced Private Sector Engagement for Tuberculosis Diagnosis and Reporting through an Intermediary Agency in Ho Chi Minh City, Viet Nam We tabulated descriptive statistics for private provider engagement and participation, the number and proportion of referred people progressing through the study''s TB care cascade by intervention district and the private TB treatment reported to our study. The study identified 1203 people with TB of whom 7.6% (91/1203) were referred and linked to care with the NTP (Figure 2 ), while 92.4% (1112/1203) consisted of private TB treatment reports and remained un-notified (Table 3) . Table 4 and Figure 3 summarize changes in the NTP''s TB case notifications in the study''s intervention area and present the modeled impact of including private TB treatment on official notification statistics. Our pilot study showed that the PPIA model was effective in engaging a large number of private providers in the Vietnamese urban setting to contribute to TB care and prevention efforts. abstract: Under-detection and -reporting in the private sector constitute a major barrier in Viet Nam’s fight to end tuberculosis (TB). Effective private-sector engagement requires innovative approaches. We established an intermediary agency that incentivized private providers in two districts of Ho Chi Minh City to refer persons with presumptive TB and share data of unreported TB treatment from July 2017 to March 2019. We subsidized chest x-ray screening and Xpert MTB/RIF testing, and supported test logistics, recording, and reporting. Among 393 participating private providers, 32.1% (126/393) referred at least one symptomatic person, and 3.6% (14/393) reported TB patients treated in their practice. In total, the study identified 1203 people with TB through private provider engagement. Of these, 7.6% (91/1203) were referred for treatment in government facilities. The referrals led to a post-intervention increase of +8.5% in All Forms TB notifications in the intervention districts. The remaining 92.4% (1112/1203) of identified people with TB elected private-sector treatment and were not notified to the NTP. Had this private TB treatment been included in official notifications, the increase in All Forms TB notifications would have been +68.3%. Our evaluation showed that an intermediary agency model can potentially engage private providers in Viet Nam to notify many people with TB who are not being captured by the current system. This could have a substantial impact on transparency into disease burden and contribute significantly to the progress towards ending TB. url: https://doi.org/10.3390/tropicalmed5030143 doi: 10.3390/tropicalmed5030143 id: cord-345528-rk16pt0i author: Yasar, Y. title: MantisCOVID: Rapid X-Ray Chest Radiograph and Mortality Rate Evaluation With Artificial Intelligence For COVID-19 date: 2020-05-08 words: 3173 sentences: 198 pages: flesch: 58 cache: ./cache/cord-345528-rk16pt0i.txt txt: ./txt/cord-345528-rk16pt0i.txt summary: This tool delivers a rapid screening test by analyzing the X-ray Chest Radiograph scans via Artificial Intelligence (AI) and it also evaluates the mortality rate of patients with the synthesis of the patient history with the machine learning methods. A rapid analysis for the Chest X-ray (CXR) scans, CT, Infection Rate or Mortality Rate with the machine learning methods are some of the helpful tools and researchers are trying to build such tools for pre-screening COVID-19. This study defines a deployed environment 1 for rapid evaluation of the mortality rate and CXR scans via machine learning tools. The evaluation platform has two outputs after screening the group of patients as the prediction about the risk in COVID-19 via CXR and the mortality rate. . https://doi.org/10.1101/2020.05.04.20090779 doi: medRxiv preprint mantisCOVID cannot catch COVID-19 patient via AI elimination from CXR, the physician can change approaching style to the patient via evaluating the mortality rate. abstract: The novel coronavirus pandemic has negative impacts over the health, economy and well-being of the global population. This negative effect is growing with the high spreading rate of the virus. The most critical step to prevent the spreading of the virus is pre-screening and early diagnosis of the individuals. This results in quaranteeing the patients not to effect the healthy population. COVID-19 is the name of the disease caused by the novel coronavirus. It has a high infection rate and it is urgent to diagnose many patients as we can to prevent the spread of the virus at the early stage. Rapid diagnostic tools development is urgent to save lives. MantisCOVID is a cloud-based pre-diagnosis tool to be accessed from the internet. This tool delivers a rapid screening test by analyzing the X-ray Chest Radiograph scans via Artificial Intelligence (AI) and it also evaluates the mortality rate of patients with the synthesis of the patient history with the machine learning methods. This study reveals the methods used over the platform and evaluation of the algorithms via open datasets. url: https://doi.org/10.1101/2020.05.04.20090779 doi: 10.1101/2020.05.04.20090779 id: cord-238881-tupom7fb author: Yeh, Chun-Fu title: A Cascaded Learning Strategy for Robust COVID-19 Pneumonia Chest X-Ray Screening date: 2020-04-24 words: 3356 sentences: 179 pages: flesch: 56 cache: ./cache/cord-238881-tupom7fb.txt txt: ./txt/cord-238881-tupom7fb.txt summary: Although the recent international joint effort on making the availability of all sorts of open data, the public collection of CXR images is still relatively small for reliably training a deep neural network (DNN) to carry out COVID-19 prediction. Although the recent international joint effort on making the availability of all sorts of open data, the public collection of CXR images is still relatively small for reliably training a deep neural network (DNN) to carry out COVID-19 prediction. Our approach leverages a large CXR image dataset of non-COVID-19 pneumonia to generalize the original well-trained classification model via a cascaded learning scheme. Our approach leverages a large CXR image dataset of non-COVID-19 pneumonia to generalize the original well-trained classification model via a cascaded learning scheme. To extend the model, we collaborate with several medical research centers in Taiwan to collect chest x-ray images from COVID-19 patients at various stages, and re-train the pneumonia classification system using a three-stage cascaded learning strategy. abstract: We introduce a comprehensive screening platform for the COVID-19 (a.k.a., SARS-CoV-2) pneumonia. The proposed AI-based system works on chest x-ray (CXR) images to predict whether a patient is infected with the COVID-19 disease. Although the recent international joint effort on making the availability of all sorts of open data, the public collection of CXR images is still relatively small for reliably training a deep neural network (DNN) to carry out COVID-19 prediction. To better address such inefficiency, we design a cascaded learning strategy to improve both the sensitivity and the specificity of the resulting DNN classification model. Our approach leverages a large CXR image dataset of non-COVID-19 pneumonia to generalize the original well-trained classification model via a cascaded learning scheme. The resulting screening system is shown to achieve good classification performance on the expanded dataset, including those newly added COVID-19 CXR images. url: https://arxiv.org/pdf/2004.12786v2.pdf doi: nan id: cord-327257-doygrgrc author: Zhu, Jocelyn title: Deep transfer learning artificial intelligence accurately stages COVID-19 lung disease severity on portable chest radiographs date: 2020-07-28 words: 3686 sentences: 221 pages: flesch: 50 cache: ./cache/cord-327257-doygrgrc.txt txt: ./txt/cord-327257-doygrgrc.txt summary: title: Deep transfer learning artificial intelligence accurately stages COVID-19 lung disease severity on portable chest radiographs This study employed deep-learning convolutional neural networks to stage lung disease severity of Coronavirus Disease 2019 (COVID-19) infection on portable chest x-ray (CXR) with radiologist score of disease severity as ground truth. Deep-learning convolutional neural network (CNN) was used to predict lung disease severity scores. In conclusion, deep-learning CNN accurately stages disease severity on portable chest x-ray of COVID-19 lung infection. This study tested the hypothesis that deep-learning convolutional neural networks accurately stage disease severity on portable chest x-rays using radiologists'' severity scores as ground truths associated with COVID-19 lung infection. Deep-learning AI, specifically a convolutional neural network, is well suited to extract information from CXR and stage disease severity by training using chest radiologist determination of disease severity scores. In conclusion, deep-learning convolutional neural networks accurately stage lung disease severity on portable chest x-rays associated with COVID-19 lung infection. abstract: This study employed deep-learning convolutional neural networks to stage lung disease severity of Coronavirus Disease 2019 (COVID-19) infection on portable chest x-ray (CXR) with radiologist score of disease severity as ground truth. This study consisted of 131 portable CXR from 84 COVID-19 patients (51M 55.1±14.9yo; 29F 60.1±14.3yo; 4 missing information). Three expert chest radiologists scored the left and right lung separately based on the degree of opacity (0–3) and geographic extent (0–4). Deep-learning convolutional neural network (CNN) was used to predict lung disease severity scores. Data were split into 80% training and 20% testing datasets. Correlation analysis between AI-predicted versus radiologist scores were analyzed. Comparison was made with traditional and transfer learning. The average opacity score was 2.52 (range: 0–6) with a standard deviation of 0.25 (9.9%) across three readers. The average geographic extent score was 3.42 (range: 0–8) with a standard deviation of 0.57 (16.7%) across three readers. The inter-rater agreement yielded a Fleiss’ Kappa of 0.45 for opacity score and 0.71 for extent score. AI-predicted scores strongly correlated with radiologist scores, with the top model yielding a correlation coefficient (R(2)) of 0.90 (range: 0.73–0.90 for traditional learning and 0.83–0.90 for transfer learning) and a mean absolute error of 8.5% (ranges: 17.2–21.0% and 8.5%-15.5, respectively). Transfer learning generally performed better. In conclusion, deep-learning CNN accurately stages disease severity on portable chest x-ray of COVID-19 lung infection. This approach may prove useful to stage lung disease severity, prognosticate, and predict treatment response and survival, thereby informing risk management and resource allocation. url: https://doi.org/10.1371/journal.pone.0236621 doi: 10.1371/journal.pone.0236621 id: cord-015352-2d02eq3y author: nan title: ESPR 2017 date: 2017-04-26 words: 82253 sentences: 4479 pages: flesch: 46 cache: ./cache/cord-015352-2d02eq3y.txt txt: ./txt/cord-015352-2d02eq3y.txt summary: Lapierre; Montreal/CA Summary: Objectives: To review the classification of visceroatrial situs To describe the associated cardiac and non-cardiac anomalies To illustrate typical findings in fetuses, neonates and children To discuss the surgical consideration and the long-term follow-up in these patients Abstract: By definition, the type of situs is determined by the relationship between the atria and the adjacent organs. As is often the case, radiology in JIA is all about: knowing your clinicians (i.e. the pretest likelihood for disease) being technically eloquent (e.g. using high-resolution US probes, not delaying post-contrast MRI acquisitions) knowing what is normal (e.g. normal undulations in the articular surface, focal bone marrow signal variation) not being dogmatic about individual observations or measurements interpreting your findings in a clinical context The lecture will demonstrate similarities and differences among joints and modalities in children with variable-severity JIA. abstract: nan url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7103096/ doi: 10.1007/s00247-017-3820-2 ==== make-pages.sh questions [ERIC WAS HERE] ==== make-pages.sh search /data-disk/reader-compute/reader-cord/bin/make-pages.sh: line 77: /data-disk/reader-compute/reader-cord/tmp/search.htm: No such file or directory Traceback (most recent call last): File "/data-disk/reader-compute/reader-cord/bin/tsv2htm-search.py", line 51, in with open( TEMPLATE, 'r' ) as handle : htm = handle.read() FileNotFoundError: [Errno 2] No such file or directory: '/data-disk/reader-compute/reader-cord/tmp/search.htm' ==== make-pages.sh topic modeling corpus Zipping study carrel