Image based artificial intelligence-enhanced ECG prediction of incident atrial fibrillation.

Abstract

Background: Early prediction of atrial fibrillation (AF) is crucial for reducing adverse outcomes. While artificial intelligence-enhanced ECG (AI-ECG) analysis shows promise in predicting AF, most approaches require digital ECG signals, limiting their application in settings where ECGs are stored as images.

Objective: We aimed to develop and validate an image-based AI-ECG approach for predicting incident AF across multiple datasets.

Methods: We used 1,163,401 ECGs from 189,539 patients in the Beth Israel Deaconess Medical Center (BIDMC) dataset and 70,655 ECGs from 65,610 participants in the UK Biobank. The AI-ECG model was trained on ECG images processed to 310x868 pixels.

Results: The model achieved C-statistics of 0.754 (95% CI: 0.747-0.761) in the BIDMC dataset and 0.723 (95% CI: 0.704-0.741) in the UK Biobank for predicting incident AF. Performance was maintained across key subgroups including outpatients, females, and non-White individuals. Compared to the CHARGE-AF risk score, the AI-ECG model showed superior performance (c-statistic 0.696 vs 0.667, p<0.05) and provided significant additive value when combined (c-statistic 0.711, p<0.0001). The model also performed well on smartphone-photographed ECGs (c-statistic 0.736). Saliency mapping indicated the model primarily focused on P-wave morphology and PR interval regions.

Conclusion: This image-based approach enables AI-ECG prediction of AF in settings without digital ECG infrastructure and provides additive value to known clinical risk scores.