id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-309660-s8neq5x4	Arntfield, R.	Development of a deep learning classifier to accurately distinguish COVID-19 from look-a-like pathology on lung ultrasound	2020-10-15		.txt	text/plain	4011	222	48	In this study, we trained a neural network using LUS images of B lines from 3 different etiologies (hydrostatic pulmonary edema (HPE), ARDS and COVID-19). 16 The goal of this study was to determine if a deep neural network could distinguish between the B line profiles of 3 different disease profiles, namely 1) hydrostatic pulmonary edema (HPE); 2) non-COVID ARDS (NCOVID) causes; and 3) COVID-19 ARDS (COVID). On this independent data, the model demonstrated a strong ability to distinguish between the 3 relevant causes of B lines with AUCs at the encounter level of 1.0 (COVID), 0.934 (NCOVID), and 1.0 (HPE), producing an overall AUC of 0.978 for the classifier. In this study, a deep learning model was successfully trained to distinguish the underlying pathology in similar point-of-care lung ultrasound images containing B lines.	./cache/cord-309660-s8neq5x4.txt	./txt/cord-309660-s8neq5x4.txt