Identifying Aortic Stenosis With a Single Parasternal Long-Axis Video Using Deep Learning.

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Bibliographic Details
Title: Identifying Aortic Stenosis With a Single Parasternal Long-Axis Video Using Deep Learning.
Authors: Dai W; Department of Electrical Engineering and Computer Science, Research Laboratory of Electronics, and Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts., Nazzari H; Department of Cardiology, Surrey Memorial Hospital, Surrey, British Columbia, Canada; University of British Columbia, Vancouver, British Columbia, Canada., Namasivayam M; Department of Cardiology, St. Vincent's Hospital, Sydney, Australia; Faculty of Medicine and Health, University of New South Wales, Sydney, Australia; Heart Valve Disease and Artificial Intelligence Laboratory, Victor Chang Cardiac Research Institute, Sydney, Australia., Hung J; Division of Cardiology, Massachusetts General Hospital, Boston, Massachusetts., Stultz CM; Department of Electrical Engineering and Computer Science, Research Laboratory of Electronics, and Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts; Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, Massachusetts; Division of Cardiology, Massachusetts General Hospital, Boston, Massachusetts. Electronic address: cmstultz@mit.edu.
Source: Journal of the American Society of Echocardiography : official publication of the American Society of Echocardiography [J Am Soc Echocardiogr] 2023 Jan; Vol. 36 (1), pp. 116-118. Date of Electronic Publication: 2022 Oct 30.
Publication Type: Journal Article; Research Support, Non-U.S. Gov't
Journal Info: Publisher: Mosby-Year Book Country of Publication: United States NLM ID: 8801388 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1097-6795 (Electronic) Linking ISSN: 08947317 NLM ISO Abbreviation: J Am Soc Echocardiogr Subsets: MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:1097-6795
DOI:10.1016/j.echo.2022.10.014