Automated Detection of Reduced Ejection Fraction Using an ECG-Enabled Digital Stethoscope: A Large Cohort Validation.

ABSTRACT:

Background

Asymptomatic left ventricular systolic dysfunction (ALVSD) affects 7 million globally, leading to delayed diagnosis and treatment, high mortality, and substantial downstream health care costs. Current detection methods for ALVSD are inadequate, necessitating the development of improved diagnostic tools. Recently, electrocardiogram-based algorithms have shown promise in detecting ALVSD.

Objectives

The authors developed and validated a convolutional neural network (CNN) model using single-lead electrocardiogram and phonocardiogram inputs captured by a digital stethoscope to assess its utility in detecting individuals with actionably low ejection fractions (EF) in a large cohort of patients.

Methods

2,960 adults undergoing echocardiography from 4 U.S. health c

SUBMITTER: Guo L 

PROVIDER: S-EPMC11891699 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature