Electrocardiogram-Based Artificial Intelligence to Identify Coronary Artery Disease.

ABSTRACT:

Background

Coronary artery disease (CAD) results in substantial morbidity and mortality.

Objectives

The purpose of this study was to develop a deep learning model to detect CAD defined using diagnostic codes ("ECG2CAD") and identify people at risk for adverse events using electrocardiograms (ECGs) in a primary care setting.

Methods

ECG2CAD was trained on 764,670 ECGs representing 137,199 individuals at Massachusetts General Hospital (MGH). Model performance for discrimination of prevalent CAD was measured using area under the receiver operating characteristic curve (AUROC) and area under the precision-recall curve (AUPRC), and compared against model of age and sex, and Pooled Cohort Equations, in 3 test sets: MGH, Brigham and Women's Hospital (BWH), and UK Biobank. Su

SUBMITTER: Kany S 

PROVIDER: S-EPMC12337187 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature