Open AccessUsing Deep-Learning Algorithms to Simultaneously Identify Right and Left Ventricular Dysfunction From the Electrocardiogram
Author(s) -
Akhil Vaid,
Kipp W. Johnson,
Marcus A. Badgeley,
Sulaiman Somani,
Mesude Bicak,
Isotta Landi,
Adam Russak,
Shan Zhao,
Matthew A. Levin,
Robert Freeman,
Alexander W. Charney,
Atul Kukar,
Bette Kim,
Tatyana Danilov,
Stamatios Lerakis,
Edgar Argulian,
Jagat Narula,
Girish N. Nadkarni,
Benjamin S. Glicksberg
Publication year - 2021
Publication title -
jacc. cardiovascular imaging
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 5.79
H-Index - 120
eISSN - 1936-878X
pISSN - 1876-7591
DOI - 10.1016/j.jcmg.2021.08.004
Subject(s) - ejection fraction , medicine , cardiology , ventricular function , heart failure , population , algorithm , computer science , environmental health
This study sought to develop DL models capable of comprehensively quantifying left and right ventricular dysfunction from ECG data in a large, diverse population.
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