id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-286485-tt9ysg0w	Lucius, M.	ROBUST COVID-19-RELATED CONDITION CLASSIFICATION NETWORK	2020-05-26		.txt	text/plain	3261	180	47	Our study evaluates the performance of a tailor-designed deep convolutional network on the tasks of early detection and localization of radiological signs associated to COVID-19 on frontal chest X-rays. The associated results show that our AI framework is able to classify COVID-19 accurately, making of it a potential tool to improve the diagnostic performance across primary-care centres and, to grant priority to a subset of algorithmic selected images for urgent follow-on expert review. However, chest x-rays taken in patients with confirmed and symptomatic COVID-19 condition can induce to confusion in cases associated to other lung infections or pathologies (including the absence of them) making it difficult for non-trained physicians to differentiate among these patterns. In all cases, the files include anonymous frontal chest X-rays, whilst the dataset provided by HM Hospitales contains anonymized records related to the 2,307 patients admitted with a confirmed (n=2,075) or pending (n=232) of COVID-19 diagnosis performed by rt-PCR.	./cache/cord-286485-tt9ysg0w.txt	./txt/cord-286485-tt9ysg0w.txt