Deep Learning-Based Identification of Echocardiographic Abnormalities From Electrocardiograms

Background: Heart failure should be diagnosed as early as possible. Although deep learning models can predict one or more echocardiographic findings from electrocardiograms (ECGs), such analyses are not comprehensive. Objectives: This study aimed to develop a deep learning model for comprehensive pr...

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Detalles Bibliográficos

Principais autores:	Goro Fujiki, MD, Satoshi Kodera, MD, PhD, Naoto Setoguchi, MD, Kengo Tanabe, MD, PhD, Kotaro Miyaji, MD, PhD, Shunichi Kushida, MD, PhD, Mike Saji, MD, PhD, Mamoru Nanasato, MD, PhD, Hisataka Maki, MD, PhD, Hideo Fujita, MD, PhD, Nahoko Kato, MD, PhD, Hiroyuki Watanabe, MD, PhD, Minami Suzuki, MD, Masao Takahashi, MD, PhD, Naoko Sawada, MD, PhD, Jiro Ando, MD, Masataka Sato, MD, Shinnosuke Sawano, MD, PhD, Susumu Katsushika, MD, PhD, Hiroki Shinohara, MD, PhD, Norifumi Takeda, MD, PhD, Katsuhito Fujiu, MD, PhD, Hiroshi Akazawa, MD, PhD, Hiroyuki Morita, MD, PhD, Issei Komuro, MD, PhD
Formato:	Artigo
Idioma:	Inglês
Publicado:	Elsevier 2025-01-01
Series:	JACC: Asia
Assuntos:	cardiac abnormalities deep learning echocardiography electrocardiography valvular heart disease
Acceso en liña:	http://www.sciencedirect.com/science/article/pii/S2772374724004241
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