Abstract
BACKGROUND: 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.
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 United Kingdom (UK) Biobank. The AI-ECG model was trained on ECG images processed to 310×868 pixels.
RESULTS: 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.
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.