Image based artificial intelligence-enhanced electrocardiogram prediction of incident atrial fibrillation

Early prediction of atrial fibrillation (AF) is crucial for reducing adverse outcomes. While artificial intelligence-enhanced electrocardiogram (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.

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

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 United Kingdom (UK) Biobank. The AI-ECG model was trained on ECG images processed to 310x868 pixels.

The model achieved C-statistics of 0.754 (95% confidence interval [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, women, and non-white individuals. Compared with the CHARGE-AF risk score, the AI-ECG model showed superior performance (c-statistic 0.696 vs 0.667, P < .05) and provided significant additive value when combined (c-statistic 0.711, P < .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.

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.