Artificial intelligence-enabled electrocardiographic screening for left ventricular systolic dysfunction and mortality risk prediction

Left ventricular systolic dysfunction (LVSD) characterized by a reduced left ventricular ejection fraction (LVEF) is associated with adverse patient outcomes. We aimed to build a deep neural network (DNN)-based model using standard 12-lead electrocardiogram (ECG) to screen for LVSD and stratify pati...

Full description

Saved in:
Bibliographic Details
Published in:Frontiers in cardiovascular medicine 2023-03, Vol.10, p.1070641-1070641
Main Authors: Huang, Yu-Chang, Hsu, Yu-Chun, Liu, Zhi-Yong, Lin, Ching-Heng, Tsai, Richard, Chen, Jung-Sheng, Chang, Po-Cheng, Liu, Hao-Tien, Lee, Wen-Chen, Wo, Hung-Ta, Chou, Chung-Chuan, Wang, Chun-Chieh, Wen, Ming-Shien, Kuo, Chang-Fu
Format: Article
Language:English
Subjects:
all-cause mortality
Cardiovascular Medicine
deep neural network
electrocardiogram
left ventricular ejection fraction
left ventricular systolic dysfunction
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Left ventricular systolic dysfunction (LVSD) characterized by a reduced left ventricular ejection fraction (LVEF) is associated with adverse patient outcomes. We aimed to build a deep neural network (DNN)-based model using standard 12-lead electrocardiogram (ECG) to screen for LVSD and stratify patient prognosis. This retrospective chart review study was conducted using data from consecutive adults who underwent ECG examinations at Chang Gung Memorial Hospital in Taiwan between October 2007 and December 2019. DNN models were developed to recognize LVSD, defined as LVEF
ISSN:2297-055X
2297-055X
DOI:10.3389/fcvm.2023.1070641