id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-327257-doygrgrc	Zhu, Jocelyn	Deep transfer learning artificial intelligence accurately stages COVID-19 lung disease severity on portable chest radiographs	2020-07-28		.txt	text/plain	3686	221	50	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.	./cache/cord-327257-doygrgrc.txt	./txt/cord-327257-doygrgrc.txt