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

ABSTRACT:

Background

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.

Methods

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 <40%, using original ECG signals or transformed images from 190,359 patients with paired ECG and echocardiogram within 14 days. The 190,359 patients were divided into a training set of 133,2

SUBMITTER: Huang YC 

PROVIDER: S-EPMC10029758 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature