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.