Integration of clinical data with scanned ECGs using deep learning methods for stroke risk prediction in Indian patients with atrial fibrillation: evidence from the KERALA-AF study.

Background: Stroke risk stratification in patients with atrial fibrillation (AF) is challenging, particularly in under-represented South Asian populations. The use of a multimodal deep-learning artificial intelligence (AI) model, which integrates clinical data with widely available paper electrocardiogram (ECG) images, represents a novel predictive approach that has not previously been validated in this population. Methods: This study used data from the prospective KERALA-AF registry, the largest prospective AF study in South Asia. We developed a multimodal deep-learning AI model to predict incident stroke within one year by combining tabular clinical data with scanned paper ECGs. We benchmarked its performance (AUC) against machine learning (ML) models using only clinical data and the CHA2DS2-VASc score. Findings: Of 631 patients included (mean age 64.4, SD 12.9; 54.2% female), 25 (4.0%) experienced a stroke within one year. The multimodal deep learning AI model incorporating ECG data achieved the highest discrimination (AUC 0.816, 95% CI 0.704-0.914), substantially outperforming the CHA2DS2-VASc score (AUC 0.666) and all compared machine learning models trained on clinical data alone. Permutation analysis showed the scanned paper ECG images contributed 57.1% of the model's predictive signal, which boosted the model's performance significantly. Interpretation: Integrating scanned paper ECGs with clinical data via deep learning methods significantly enhanced 1-year stroke clinical risk prediction in South Asian AF patients. This study demonstrates the value of using multimodal AI with readily available, non-digital data in improving clinical risk stratification beyond current approaches based on clinical risk factors alone. Funding: Kerala Chapter of Cardiological Society of India.