Electrocardiogram-based deep learning to predict mortality in paediatric and adult congenital heart disease.

ABSTRACT:

Background and aims

Robust and convenient risk stratification of patients with paediatric and adult congenital heart disease (CHD) is lacking. This study aims to address this gap with an artificial intelligence-enhanced electrocardiogram (ECG) tool across the lifespan of a large, diverse cohort with CHD.

Methods

A convolutional neural network was trained (50%) and tested (50%) on ECGs obtained in cardiology clinic at the Boston Children's Hospital to detect 5-year mortality. Temporal validation on a contemporary cohort was performed. Model performance was evaluated using the area under the receiver operating characteristic and precision-recall curves.

Results

The training and test cohorts composed of 112 804 ECGs (39 784 patients; ECG age range 0-85 years; 4.9% 5-year

SUBMITTER: Mayourian J 

PROVIDER: S-EPMC11879189 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature