id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-260167-3kjjjbp0	Kusunose, Kenya	Steps to use artificial intelligence in echocardiography	2020-10-12		.txt	text/plain	3062	190	42	Such examples of this are AI-developed computed tomography and magnetic resonance image measurement of lumen diameter, recognition of coronary calcium score, recognition of obstructive coronary disease, automated acquisition, segmentation, and report generation [7] [8] [9] . This proposed approach could also be generalized to other images involving deep learning in the cardiovascular field, where there are frequent gaps in clinical labeling [16] . Recently, we reported on our newly developed view classification model, based on convolutional neural network using 17,000 images. Recently, our group developed an AI model for automated detection of RWMAs in myocardial infarction, using a deep learning algorithm including ResNet, DenseNet, Inception-ResNet, Inception, and Xception for a convolutional neural network [31] [32] [33] . A deep learning approach for assessment of regional wall motion abnormality from echocardiographic images Clinically feasible and accurate view classification of echocardiographic images using deep learning	./cache/cord-260167-3kjjjbp0.txt	./txt/cord-260167-3kjjjbp0.txt