Prediction of Atrial Fibrillation From the ECG in the Community Using Deep Learning: A Multinational Study.

ABSTRACT:

Background

We aimed to refine and validate a deep neural network model from the ECG to predict atrial fibrillation (AF) risk, using samples from diverse backgrounds: the Framingham Heart Study (FHS), UK Biobank, and Estudo Longitudinal da Saúde do Adulto (ELSA-Brasil). We compared the model's performance to the clinical Cohorts for Heart and Aging Research in Genomic Epidemiology consortium (CHARGE-AF) risk score and evaluated the association with other cardiovascular outcomes.

Methods

The ECG-derived deep-learning prediction of AF (ECG-AF) model was refined using 60% of FHS samples free of AF. Its performance was then tested in the remaining FHS samples, UK Biobank, and ELSA-Brasil, with discrimination assessed by the area under the receiver operating characteristic curve.

SUBMITTER: Brant LCC 

PROVIDER: S-EPMC12569998 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature