id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-346483-jc0xklzk	Chen, Jun	Deep learning-based model for detecting 2019 novel coronavirus pneumonia on high-resolution computed tomography	2020-11-05		.txt	text/plain	3723	208	49	For model development and validation, 46,096 anonymous images from 106 admitted patients, including 51 patients of laboratory confirmed COVID-19 pneumonia and 55 control patients of other diseases in Renmin Hospital of Wuhan University were retrospectively collected. Fig. 1 , a total of 46,096 CT scan images from 51 COVID-19 pneumonia patients and 55 control patients of other disease from Renmin Hospital of Wuhan University were collected for developing the model to detect COVID-19 pneumonia. However, compared to the needs of the patients, the number of radiologists is quite small, especially in Hubei province, China, which could greatly delay the diagnosis and isolation of patients, affect patient's treatment and prognosis, and ultimately, affect the overall control of COVID-19 epidemic. In the present study, our model helped expert radiologists achieve the same work with much shorter time, which greatly accelerats the efficiency of diagnosis in clinical practice, and may contribute to the improvement of patient outcome.	./cache/cord-346483-jc0xklzk.txt	./txt/cord-346483-jc0xklzk.txt