BibTex format
@article{Zeidaabadi:2026:10.1016/j.hrthm.2025.10.024,
author = {Zeidaabadi, B and Patlatzoglou, K and Barker, J and Pastika, L and Khattak, GR and Gurnani, M and Machado, XDSA and Peters, NS and Kramer, DB and Waks, JW and Ng, FS and Sau, A},
doi = {10.1016/j.hrthm.2025.10.024},
journal = {Heart Rhythm},
pages = {515--524},
title = {Image based artificial intelligence-enhanced ECG prediction of incident atrial fibrillation},
url = {http://dx.doi.org/10.1016/j.hrthm.2025.10.024},
volume = {23},
year = {2026}
}
RIS format (EndNote, RefMan)
TY - JOUR
AB - BackgroundEarly 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.ObjectiveWe aimed to develop and validate an image-based AI-ECG approach for predicting incident AF across multiple datasets.MethodsWe 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.ResultsThe 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.ConclusionThis image-based approach enables AI-ECG prediction of AF in settings without digital ECG infrastructure and provides additive value to known clinical risk scores
AU - Zeidaabadi,B
AU - Patlatzoglou,K
AU - Barker,J
AU - Pastika,L
AU - Khattak,GR
AU - Gurnani,M
AU - Machado,XDSA
AU - Peters,NS
AU - Kramer,DB
AU - Waks,JW
AU - Ng,FS
AU - Sau,A
DO - 10.1016/j.hrthm.2025.10.024
EP - 524
PY - 2026///
SN - 1547-5271
SP - 515
TI - Image based artificial intelligence-enhanced ECG prediction of incident atrial fibrillation
T2 - Heart Rhythm
UR - http://dx.doi.org/10.1016/j.hrthm.2025.10.024
UR - https://doi.org/10.1016/j.hrthm.2025.10.024
VL - 23
ER -