id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-347333-h899xkfy	Li, Z.	From Community Acquired Pneumonia to COVID-19: A Deep Learning Based Method for Quantitative Analysis of COVID-19 on thick-section CT Scans	2020-04-23		.txt	text/plain	2963	180	57	Materials and Methods: In this retrospective study, a deep learning based system was developed to automatically segment and quantify the COVID-19 infected lung regions on thick-section chest CT images. Conclusions: A deep learning based AI system built on the thick-section CT imaging can accurately quantify the COVID-19 associated lung abnormalities, assess the disease severity and its progressions. 8 The explosive growing number of COVID-19 patients requires the automated AI-based computer 9 aided diagnosis (CAD) systems that can accurately and objectively detect the disease infected lung 10 regions, assess the severity and the progressions. For evaluation, the AI based lung abnormalities segmentation was compared to two 28 experienced radiologists manually delineations, while the AI based assessment of disease severity and 29 progression was compared to patients diagnosis status extracted from clinical and radiology reports. In conclusion, a deep learning based AI system is developed to quantify COVID-19 abnormal 180 lung patterns, assess the disease severity and the progression using thick-section chest CT images.	./cache/cord-347333-h899xkfy.txt	./txt/cord-347333-h899xkfy.txt