AI-ECG Risk Stratification for Atrial Fibrillation: Real-World Performance and Explainability.

BACKGROUND: Artificial intelligence-enabled electrocardiography (AI-ECG) has emerged as a potential method for identifying atrial fibrillation (AF) from sinus rhythm. However, its clinical utility and interpretability in routine practice remain uncertain. OBJECTIVES: The objective of the study was to assess the performance and explainability of an AI-integrated ECG system for AF risk stratification in a multicenter real-world cohort. METHODS: We enrolled 665 patients aged ≥40 years who underwent 12-lead ECGs using an AI-enabled electrocardiograph (FCP-9900). The device automatically assigned AF risk into 4 categories (low, mid-low, mid-high, and high). Machine-learning models-support vector machine, adaptive boosting, and artificial neural networks-were developed using clinical and ECG-derived variables. Internal validation used stratified 10-fold cross-validation, and external validation used an independent cohort. Feature contributions were assessed with SHapley Additive exPlanations. RESULTS: AF prevalence increased across AI-ECG risk categories, with significantly higher odds in the mid-high and high groups vs low. Model 2, which incorporated CHADS2 and CHA2DS2-VASc scores, achieved strong discrimination in internal and external validation (support vector machine AUC 1.00; adaptive boosting 0.97-0.98; artificial neural network 0.89-0.95), outperforming AI-ECG alone (area under the receiver operating characteristic curve: 0.64-0.69). SHapley Additive exPlanations analysis showed CHA2DS2-VASc as the most influential predictor, whereas AI-ECG provided modest incremental value. CONCLUSIONS: AI-ECG provides rapid, low-cost AF risk estimation from a single sinus rhythm ECG; however, its predictive performance is modest compared with clinical scores. At present, AI-ECG may complement, but not replace, traditional risk stratification.