An AI model analyzing handheld single-lead ECGs predicted incident atrial fibrillation with an AUROC of 0.695, performing comparably to the 11-variable CHARGE-AF clinical risk score.
Does AI analysis of handheld single-lead ECGs predict incident atrial fibrillation in adults?
AI analysis of handheld single-lead ECGs can predict incident atrial fibrillation as effectively as traditional risk scores, offering a scalable approach for risk-informed screening beyond simple age thresholds.
Absolute Event Rate: 0.695% vs 0.679%
Atrial fibrillation (AF) affects over 50 million people worldwide and carries substantial downstream morbidity, mortality, and cost. Yet many contemporary screening programs rely primarily on age thresholds—an approach that is operationally simple but can be imprecise for identifying near-term risk. AI applied to handheld single-lead ECGs can predict incident AF with accuracy similar to established clinical risk scores, but real-world deployment remains limited by signal noise, workflow complexity, and unclear risk thresholds.
Menon et al. (Thu,) conducted a editorial in Atrial fibrillation. 1L ECG-AI model combined with age and sex (1L ECG-AI AS) vs. CHARGE-AF clinical risk score was evaluated on Prediction of incident atrial fibrillation (AUROC) (95% CI 0.637-0.742). An AI model analyzing handheld single-lead ECGs predicted incident atrial fibrillation with an AUROC of 0.695, performing comparably to the 11-variable CHARGE-AF clinical risk score.