Artificial intelligence predicts undiagnosed atrial fibrillation in patients with embolic stroke of undetermined source using sinus rhythm electrocardiograms

Background Artificial intelligence (AI)-enabled sinus rhythm (SR) electrocardiogram (ECG) interpretation can aid in identifying undiagnosed paroxysmal atrial fibrillation (AF) in patients with embolic stroke of undetermined source (ESUS). Objective We assessed the efficacy of an AI model in identifying AF based on SR ECGs in patients with ESUS. Methods A transformer-based vision AI model was developed using 737,815 SR ECGs from patients with and without AF to detect current paroxysmal AF or predict the future development of AF within a two-year period. Probability of AF was calculated from baseline SR ECGs using this algorithm. Its diagnostic performance was further tested in a cohort of 352 ESUS patients from four tertiary hospitals, all of whom were monitored using an insertable cardiac monitor (ICM) for AF surveillance. Results Over a 25.1-month follow-up, AF episodes lasting ≥1hr were identified in 58 patients (14.4%) using ICMs. In the receiver operating curve (ROC) analysis, the area under the curve (AUC) for the AI algorithm to identify AF ≥1hr was 0.806, which improved to 0.880 after integrating the clinical parameters into the model. The AI algorithm exhibited greater accuracy in identifying longer AF episodes (ROC for AF ≥12hr: 0.837, AF ≥24hr: 0.879), alongside a temporal trend indicating that the AI-AF risk score increased as the ECG recording approached the AF onset (p for trend <0.0001). Conclusions Our AI model demonstrated excellent diagnostic performance in predicting AF in patients with ESUS, potentially enhancing patient prognosis through timely intervention and secondary prevention of ischemic stroke in ESUS cohorts.