Image based artificial intelligence-enhanced electrocardiogram prediction of incident atrial fibrillation
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.
Authors
- Joseph Barker (ORCID: https://orcid.org/0000-0002-5483-9608)
- Konstantinos Patlatzoglou (ORCID: https://orcid.org/0000-0002-5888-8490)
- Libor Pastika (ORCID: https://orcid.org/0000-0001-6892-6553)
- Daniel B. Kramer (ORCID: https://orcid.org/0000-0003-4241-3586)
- Nicholas S. Peters (ORCID: https://orcid.org/0000-0002-1014-7052)
- Arunashis Sau (ORCID: https://orcid.org/0000-0002-0204-7078)
- Gul Rukh Khattak (ORCID: https://orcid.org/0000-0003-0674-2833)
- Fu Siong Ng (ORCID: https://orcid.org/0000-0002-8681-4368)
- Jonathan W. Waks (ORCID: https://orcid.org/0000-0001-5560-5638)
- Mehak Gurnani (ORCID: https://orcid.org/0009-0003-9228-2518)
- Boroumand Zeidaabadi (ORCID: https://orcid.org/0009-0004-0025-9896)
- Xavier Da Silva Anjos Machado
Institutions
- Beth Israel Deaconess Medical Center (US)
- Harvard University (US)
- Imperial College Healthcare NHS Trust (GB)
- NIHR Imperial Biomedical Research Centre (GB)
- Deaconess Hospital (US)
- Imperial College London (GB)
Publication Details
- Journal
- Heart Rhythm
- Published
- 2025-10-15
- DOI
- https://doi.org/10.1016/j.hrthm.2025.10.024
- Primary Topic
- ECG Monitoring and Analysis
- Type
- article
- Field-Weighted Citation Impact
- 0.00