Health AI Central
Peer-Reviewed Publication
Heart Rhythm2026;23(3):515-524.March 1, 2026Journal Article

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

Boroumand Zeidaabadi1, Konstantinos Patlatzoglou1, Joseph Barker1, Libor Pastika1, Gul Rukh Khattak1, Mehak Gurnani1, Xavier Da Silva Anjos Machado1, Nicholas S Peters2, Daniel B Kramer3, Jonathan W Waks4, Fu Siong Ng5, Arunashis Sau6
1National Heart and Lung Institute, Imperial College London, United Kingdom.
2National Heart and Lung Institute, Imperial College London, United Kingdom; Department of Cardiology, Imperial College Healthcare NHS Trust, London, United Kingdom.
3National Heart and Lung Institute, Imperial College London, United Kingdom; Richard A. and Susan F. Smith Center for Outcomes Research in Cardiology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts.
4Harvard-Thorndike Electrophysiology Institute, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts.
5National Heart and Lung Institute, Imperial College London, United Kingdom; Department of Cardiology, Imperial College Healthcare NHS Trust, London, United Kingdom; Department of Cardiology, Chelsea and Westminster Hospital NHS Foundation Trust, London, United Kingdom. Electronic address: f.ng@imperial.ac.uk.
6National Heart and Lung Institute, Imperial College London, United Kingdom; Department of Cardiology, Imperial College Healthcare NHS Trust, London, United Kingdom. Electronic address: as7909@imperial.ac.uk.
PubMed DOI

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 f…

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