Prediction of Atrial Fibrillation Risk Through the Integration of Genetic Information and Artificial Intelligence-Based Electrocardiogram Data

Abstract

Background Accurate prediction of atrial fibrillation (AF) is essential for prevention. The CHARGE-AF score, based on routinely available clinical factors, provides a practical tool for estimating AF risk but has limited predictive accuracy.