# ent

id: cord-157444-huvnyali
type: WORK_OF_ART
entity:
  Deep Learning for Distinguishing Normal versus Abnormal Chest Radiographs and Generalization to Unseen Diseases


id: cord-157444-huvnyali
type: WORK_OF_ART
entity:
  Training and Validating a Deep Convolutional Neural Network for Computer Aided Detection and Classification of Abnormalities on Frontal Chest


id: cord-292341-uo54ghf3
type: ORG
entity:
  the University Hospital of Padova Division of Infectious and Tropical Diseases Respiratory Disease Unit


id: cord-006708-nionk55w
type: PROTEIN
entity:
  alkaline phosphatase ALP alanine aminotransferase ALT aspartate aminotransferase AST gamma glutamyltransferase GGT creatine kinase CK


id: cord-238881-tupom7fb
type: PERSON
entity:
  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


id: cord-103840-2diao7zh
type: WORK_OF_ART
entity:
  the Global Burden of Disease Study Establishing the Cardiothoracic Ratio Using Chest Radiographs in an Indigenous Ghanaian Population A Simple Tool for Cardiomegaly


id: cord-127759-wpqdtdjs
type: DNA
entity:
  encoder network Pretrained AlexNet 23 ResNet50 24 SonoNet64 25 XNet Xception 26 InceptionV4 Inception Resnet


id: cord-018027-goxdiyv3
type: GGP
entity:
  PcP 6 7 18 33 and is a reliable method for discriminating it from other infectious processes 33 39 of ground glass opacities and intralobular septae sparing out the subpleural space i e


id: cord-018027-goxdiyv3
type: ORG
entity:
  European Organization for Research and Treatment of Cancer Invasive Fungal Infections Cooperative Group National Institute of Allergy and Infectious Diseases


id: cord-018027-goxdiyv3
type: ORG
entity:
  the European Organization for Research and Treatment of Cancer Invasive Fungal Infections Cooperative Group


id: cord-303483-wendrxee
type: FAC
entity:
  the Cruise Ship Diamond Princess Radiology Department Preparedness for COVID 19 Radiology Scientific Expert Panel COVID 19 Outbreak


id: cord-303483-wendrxee
type: LAW
entity:
  COVID 19 COVID 19 Public Health Recommendations for Community Related Exposure Guidance for Risk Assessment and Public Health Management of Healthcare Personnel with Characteristics of and Important Lessons From the Coronavirus


id: cord-269014-ck27fm58
type: ORG
entity:
  Potential Impact of the Covid 19 Response on Tuberculosis in High Burden Countries A Modelling Analysis


id: cord-269014-ck27fm58
type: ORG
entity:
  Mumbai Achieving Systemic and Scalable Private Sector Engagement in Tuberculosis Care and Prevention


id: cord-013065-oj0wsstz
type: GGP
entity:
  Cronbach s alpha A p 0 05 will be considered significant The statistical program SPPS 20 0 will be used Nowadays care standards are focused on the quality of care As the World Health Organization stipulates the patient must be diagnosed and treated correctly This clinical trial is focused on improving the quality of care for paediatric patients with suspected bacterial


id: cord-334495-7y1la856
type: ORG
entity:
  non obstructive coronary arteries Association of Cardiac Injury With Mortality in Hospitalized Patients With COVID 19


id: cord-345528-rk16pt0i
type: DISEASE
entity:
  immune system coronary heart disease chronic obstructive lung disease diabetes HIV positive hypertension high blood pressure


id: cord-297396-r1p7xn3a
type: ORG
entity:
  The Lancet Infectious Diseases Differences in Clinical and Imaging Presentation of Pediatric Patients


id: cord-327257-doygrgrc
type: ORG
entity:
  Community Acquired Pneumonia on Chest CT COVID Net A Tailored Deep Convolutional Neural Network Design for Detection of COVID 19 Cases


id: cord-336843-c0sr3six
type: WORK_OF_ART
entity:
  Imaging Findings in Acute Invasive Pulmonary Aspergillosis Clinical Significance of the Halo Sign Clinical Infectious Diseases Effective


id: cord-346942-88l03lf0
type: ORG
entity:
  National Health Commission State Administration of Traditional Chinese Medicine Diagnosis and Treatment Protocol


id: cord-015352-2d02eq3y
type: PROTEIN
entity:
  N Colavolpe A Aschero B Bourliere Najean C Roman F Khachab H Pico M Kheiri G Gorincour C Desvignes P Petit Marseille FR


id: cord-015352-2d02eq3y
type: PROTEIN
entity:
  Help E Kovacs 1 N Pinter 2 G Balázs 1 A Machovitsch 1 A Arany 1 Z Liptai 1 L Fonyad 1 P Benke 1 1 Budapest HU 2 Amherst US Objective


id: cord-015352-2d02eq3y
type: GGP
entity:
  CMD p 0 11 Linear hierarchical regressions controlling the age did not show a significant relation between ADC and cortical or renal transit times p 0 68 but lower FA values


id: cord-015352-2d02eq3y
type: DISEASE
entity:
  pulmonary artery pulmonary sequestration systemic arterialization of lung absence of inferior vena cava


id: cord-015352-2d02eq3y
type: DISEASE
entity:
  congenital pulmonary airway malformation CPAM 1 hemithorax pulmonary sequestration 1 hemithorax partial pneumothorax


# bib

id: cord-006683-7rsmbk3j
author: Coppola, M.
date: 2011-01-12
txt: ./txt/cord-006683-7rsmbk3j.txt
cache: ./cache/cord-006683-7rsmbk3j.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7102231/
doi: 10.1007/s11547-011-0622-0
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.

title:
  Influenza A virus: radiological and clinical findings of patients hospitalised for pandemic H1N1 influenza

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.


id: cord-006708-nionk55w
title: The pulmonary findings of Crimean–Congo hemorrhagic fever patients with chest X-ray assessments
author: Aktaş, Fatma
date: 2019-03-25
txt: ./txt/cord-006708-nionk55w.txt
cache: ./cache/cord-006708-nionk55w.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7102267/
doi: 10.1007/s11547-019-01024-w
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.


id: cord-013065-oj0wsstz
author: Rodríguez-Fanjul, Javier
date: 2020-10-08
txt: ./txt/cord-013065-oj0wsstz.txt
cache: ./cache/cord-013065-oj0wsstz.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7543673/
doi: 10.1186/s12931-020-01476-z
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.

title:
  Procalcitonin and lung ultrasound algorithm to diagnose severe pneumonia in critical paediatric patients (PROLUSP study). A randomised clinical trial

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.


id: cord-015352-2d02eq3y
title: ESPR 2017
author: nan
date: 2017-04-26
txt: ./txt/cord-015352-2d02eq3y.txt
cache: ./cache/cord-015352-2d02eq3y.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7103096/
doi: 10.1007/s00247-017-3820-2
abstract: nan
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.


id: cord-018027-goxdiyv3
title: Diagnostic Radiology in Hematological Patients with Febrile Neutropenia
author: Heussel, Claus Peter
date: 2014-11-27
txt: ./txt/cord-018027-goxdiyv3.txt
cache: ./cache/cord-018027-goxdiyv3.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7122774/
doi: 10.1007/978-3-662-44000-1_7
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.


id: cord-028786-400vglzm
title: Diagnosing Tuberculosis Using Deep Convolutional Neural Network
author: Oloko-Oba, Mustapha
date: 2020-06-05
txt: ./txt/cord-028786-400vglzm.txt
cache: ./cache/cord-028786-400vglzm.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7340926/
doi: 10.1007/978-3-030-51935-3_16
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.


id: cord-103840-2diao7zh
author: Mungai, B. N.
date: 2020-08-22
txt: ./txt/cord-103840-2diao7zh.txt
cache: ./cache/cord-103840-2diao7zh.txt
extension: .txt
mime: text/plain
url: http://medrxiv.org/cgi/content/short/2020.08.19.20177907v1?rss=1
doi: 10.1101/2020.08.19.20177907
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.

title:
  It''s not TB but what could it be? Abnormalities on chest X-rays taken during the Kenya National Tuberculosis 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).


id: cord-127759-wpqdtdjs
author: Qi, Xiao
date: 2020-11-06
txt: ./txt/cord-127759-wpqdtdjs.txt
cache: ./cache/cord-127759-wpqdtdjs.txt
extension: .txt
mime: text/plain
url: https://arxiv.org/pdf/2011.03585v1.pdf
doi: nan
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.

title:
  Chest X-ray Image Phase Features for Improved Diagnosis of COVID-19 Using Convolutional Neural Network

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.


id: cord-157444-huvnyali
author: Nabulsi, Zaid
date: 2020-10-22
txt: ./txt/cord-157444-huvnyali.txt
cache: ./cache/cord-157444-huvnyali.txt
extension: .txt
mime: text/plain
url: https://arxiv.org/pdf/2010.11375v1.pdf
doi: nan
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.

title:
  Deep Learning for Distinguishing Normal versus Abnormal Chest Radiographs and Generalization to Unseen Diseases

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.


id: cord-167889-um3djluz
title: A Survey on Applications of Artificial Intelligence in Fighting Against COVID-19
author: Chen, Jianguo
date: 2020-07-04
txt: ./txt/cord-167889-um3djluz.txt
cache: ./cache/cord-167889-um3djluz.txt
extension: .txt
mime: text/plain
url: https://arxiv.org/pdf/2007.02202v1.pdf
doi: nan
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.


id: cord-238881-tupom7fb
title: A Cascaded Learning Strategy for Robust COVID-19 Pneumonia Chest X-Ray Screening
author: Yeh, Chun-Fu
date: 2020-04-24
txt: ./txt/cord-238881-tupom7fb.txt
cache: ./cache/cord-238881-tupom7fb.txt
extension: .txt
mime: text/plain
url: https://arxiv.org/pdf/2004.12786v2.pdf
doi: nan
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.


id: cord-266672-t85wd0xq
author: Bagnera, Silvia
date: 2020-07-20
txt: ./txt/cord-266672-t85wd0xq.txt
cache: ./cache/cord-266672-t85wd0xq.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pubmed/32754375/
doi: 10.25259/jcis_76_2020
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.

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

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.


id: cord-269014-ck27fm58
author: Vo, Luan Nguyen Quang
date: 2020-09-14
txt: ./txt/cord-269014-ck27fm58.txt
cache: ./cache/cord-269014-ck27fm58.txt
extension: .txt
mime: text/plain
url: https://doi.org/10.3390/tropicalmed5030143
doi: 10.3390/tropicalmed5030143
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.

title:
  Enhanced Private Sector Engagement for Tuberculosis Diagnosis and Reporting through an Intermediary Agency in Ho Chi Minh City, Viet Nam

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.


id: cord-275974-uqd30v7b
author: Shorfuzzaman, Mohammad
date: 2020-10-17
txt: ./txt/cord-275974-uqd30v7b.txt
cache: ./cache/cord-275974-uqd30v7b.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pubmed/33100403/
doi: 10.1016/j.patcog.2020.107700
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.

title:
  MetaCOVID: A Siamese neural network framework with contrastive loss for n-shot diagnosis of COVID-19 patients

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.


id: cord-282198-ugmv9om1
title: Point-of-care Lung Ultrasound Is More Sensitive than Chest Radiograph for Evaluation of COVID-19
author: Pare, Joseph R.
date: 2020-06-19
txt: ./txt/cord-282198-ugmv9om1.txt
cache: ./cache/cord-282198-ugmv9om1.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pubmed/32726240/
doi: 10.5811/westjem.2020.5.47743
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.


id: cord-292341-uo54ghf3
title: Clinical Features and Chest Imaging as Predictors of Intensity of Care in Patients with COVID-19
author: Cocconcelli, Elisabetta
date: 2020-09-16
txt: ./txt/cord-292341-uo54ghf3.txt
cache: ./cache/cord-292341-uo54ghf3.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pubmed/32947904/
doi: 10.3390/jcm9092990
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.


id: cord-294557-4h0sybiy
title: Coronavirus disease 2019 (COVID-19) in the radiology department: What radiographers need to know
author: Stogiannos, N.
date: 2020-06-04
txt: ./txt/cord-294557-4h0sybiy.txt
cache: ./cache/cord-294557-4h0sybiy.txt
extension: .txt
mime: text/plain
url: https://www.sciencedirect.com/science/article/pii/S1078817420300845
doi: 10.1016/j.radi.2020.05.012
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.


id: cord-296208-uy1r6lt2
author: Greenspan, Hayit
date: 2020-08-19
txt: ./txt/cord-296208-uy1r6lt2.txt
cache: ./cache/cord-296208-uy1r6lt2.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pubmed/32890777/
doi: 10.1016/j.media.2020.101800
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

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

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.


id: cord-297198-dneycnyr
author: Khan, T.
date: 2020-05-27
txt: ./txt/cord-297198-dneycnyr.txt
cache: ./cache/cord-297198-dneycnyr.txt
extension: .txt
mime: text/plain
url: https://doi.org/10.1016/j.crad.2020.05.009
doi: 10.1016/j.crad.2020.05.009
abstract: nan
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.

title:
  Re: a British Society of Thoracic Imaging statement: considerations in designing local imaging diagnostic algorithms for the COVID-19 pandemic


id: cord-297396-r1p7xn3a
title: Development and Validation of Risk Prediction Models for COVID-19 Positivity in a Hospital Setting
author: Ng, Ming-Yen
date: 2020-09-15
txt: ./txt/cord-297396-r1p7xn3a.txt
cache: ./cache/cord-297396-r1p7xn3a.txt
extension: .txt
mime: text/plain
url: https://api.elsevier.com/content/article/pii/S1201971220307384
doi: 10.1016/j.ijid.2020.09.022
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.


id: cord-303483-wendrxee
author: Rubin, Geoffrey D.
date: 2020-04-07
txt: ./txt/cord-303483-wendrxee.txt
cache: ./cache/cord-303483-wendrxee.txt
extension: .txt
mime: text/plain
url: https://doi.org/10.1148/radiol.2020201365
doi: 10.1148/radiol.2020201365
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) .

title:
  The Role of Chest Imaging in Patient Management during the COVID-19 Pandemic: A Multinational Consensus Statement from the Fleischner Society

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.


id: cord-310228-bqpvykce
title: Using Artificial Intelligence for COVID-19 Chest X-ray Diagnosis
author: Borkowski, A. A.
date: 2020-05-26
txt: ./txt/cord-310228-bqpvykce.txt
cache: ./cache/cord-310228-bqpvykce.txt
extension: .txt
mime: text/plain
url: http://medrxiv.org/cgi/content/short/2020.05.21.20106518v1?rss=1
doi: 10.1101/2020.05.21.20106518
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.


id: cord-312251-t6omrr07
author: Vancheri, Sergio Giuseppe
date: 2020-05-30
txt: ./txt/cord-312251-t6omrr07.txt
cache: ./cache/cord-312251-t6omrr07.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pubmed/32474630/
doi: 10.1007/s00330-020-06967-7
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] .

title:
  Radiographic findings in 240 patients with COVID-19 pneumonia: time-dependence after the onset of symptoms

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.


id: cord-327257-doygrgrc
author: Zhu, Jocelyn
date: 2020-07-28
txt: ./txt/cord-327257-doygrgrc.txt
cache: ./cache/cord-327257-doygrgrc.txt
extension: .txt
mime: text/plain
url: https://doi.org/10.1371/journal.pone.0236621
doi: 10.1371/journal.pone.0236621
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.

title:
  Deep transfer learning artificial intelligence accurately stages COVID-19 lung disease severity on portable chest radiographs

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.


id: cord-331891-a6b1xanm
author: Cozzi, Diletta
date: 2020-06-09
txt: ./txt/cord-331891-a6b1xanm.txt
cache: ./cache/cord-331891-a6b1xanm.txt
extension: .txt
mime: text/plain
url: https://doi.org/10.1007/s11547-020-01232-9
doi: 10.1007/s11547-020-01232-9
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.

title:
  Chest X-ray in new Coronavirus Disease 2019 (COVID-19) infection: findings and correlation with clinical outcome

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.


id: cord-334495-7y1la856
title: Heart and Lung Multimodality Imaging in COVID-19
author: Agricola, Eustachio
date: 2020-06-24
txt: ./txt/cord-334495-7y1la856.txt
cache: ./cache/cord-334495-7y1la856.txt
extension: .txt
mime: text/plain
url: https://api.elsevier.com/content/article/pii/S1936878X20304770
doi: 10.1016/j.jcmg.2020.05.017
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.


id: cord-336843-c0sr3six
author: Gerritsen, M. G.
date: 2017-02-24
txt: ./txt/cord-336843-c0sr3six.txt
cache: ./cache/cord-336843-c0sr3six.txt
extension: .txt
mime: text/plain
url: https://doi.org/10.1371/journal.pone.0172256
doi: 10.1371/journal.pone.0172256
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.

title:
  Improving early diagnosis of pulmonary infections in patients with febrile neutropenia using low-dose chest computed tomography

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.


id: cord-337507-cqbbrnku
title: Chest x-ray in the COVID-19 pandemic: Radiologists’ real-world reader performance
author: Cozzi, Andrea
date: 2020-09-10
txt: ./txt/cord-337507-cqbbrnku.txt
cache: ./cache/cord-337507-cqbbrnku.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pubmed/32971326/
doi: 10.1016/j.ejrad.2020.109272
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.


id: cord-345528-rk16pt0i
author: Yasar, Y.
date: 2020-05-08
txt: ./txt/cord-345528-rk16pt0i.txt
cache: ./cache/cord-345528-rk16pt0i.txt
extension: .txt
mime: text/plain
url: https://doi.org/10.1101/2020.05.04.20090779
doi: 10.1101/2020.05.04.20090779
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.

title:
  MantisCOVID: Rapid X-Ray Chest Radiograph and Mortality Rate Evaluation With Artificial Intelligence For COVID-19

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.


id: cord-346942-88l03lf0
title: Diagnostic and Prognostic Value of Chest Radiographs for COVID-19 at Presentation
author: Kerpel, Ariel
date: 2020-08-17
txt: ./txt/cord-346942-88l03lf0.txt
cache: ./cache/cord-346942-88l03lf0.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pubmed/32970556/
doi: 10.5811/westjem.2020.7.48842
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.


id: cord-347691-ia2i8svg
title: Multimodality imaging of COVID-19 pneumonia: from diagnosis to follow-up. A comprehensive review
author: Larici, Anna Rita
date: 2020-08-17
txt: ./txt/cord-347691-ia2i8svg.txt
cache: ./cache/cord-347691-ia2i8svg.txt
extension: .txt
mime: text/plain
url: https://doi.org/10.1016/j.ejrad.2020.109217
doi: 10.1016/j.ejrad.2020.109217
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.


id: cord-350636-ufwfitue
title: Comparison of Chest Ultrasound and Standard X-Ray Imaging in COVID-19 Patients
author: Shumilov, Evgenii
date: 2020-09-02
txt: ./txt/cord-350636-ufwfitue.txt
cache: ./cache/cord-350636-ufwfitue.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pubmed/32905446/
doi: 10.1055/a-1217-1603
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.


id: cord-355218-eici4eit
author: Punn, Narinder Singh
date: 2020-10-17
txt: ./txt/cord-355218-eici4eit.txt
cache: ./cache/cord-355218-eici4eit.txt
extension: .txt
mime: text/plain
url: https://arxiv.org/pdf/2004.11676v5.pdf
doi: 10.1007/s10489-020-01900-3
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

title:
  Automated diagnosis of COVID-19 with limited posteroanterior chest X-ray images using fine-tuned deep neural networks

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.


