Deep Learning-Based Identification of Echocardiographic Abnormalities From Electrocardiograms.

ABSTRACT:

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 prediction of echocardiographic findings from ECGs.

Methods

We obtained 229,439 paired ECG and echocardiography data sets from 8 centers. Six centers contributed to model development and 2 to external validation. We identified 12 echocardiographic findings related to left-sided cardiac abnormalities, valvular heart diseases, and right-sided cardiac abnormalities. These findings were predicted using convolutional neural networks, and a composite label was analyzed using logistic

SUBMITTER: Fujiki G 

PROVIDER: S-EPMC11775793 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature