Prediction of incident atrial fibrillation using deep learning, clinical models, and polygenic scores
ECG-AI provides an accurate tool to predict new-onset AF in a tertiary cardiac centre, surpassing clinical and PGS.
Authors
- Denis Corbin (ORCID: https://orcid.org/0009-0007-4201-440X)
- Jacques Delfrate (ORCID: https://orcid.org/0009-0001-0466-1354)
- Julie Hussin (ORCID: https://orcid.org/0000-0003-4295-3339)
- Marie‐Pierre Dubé (ORCID: https://orcid.org/0000-0001-8442-4393)
- Rafik Tadros (ORCID: https://orcid.org/0000-0002-1472-0258)
- Léna Rivard (ORCID: https://orcid.org/0000-0002-1340-0850)
- Laurent Macle (ORCID: https://orcid.org/0000-0002-3328-5239)
- Paul Khairy (ORCID: https://orcid.org/0000-0003-4059-3800)
- Jean‐Claude Tardif (ORCID: https://orcid.org/0000-0002-8200-8983)
- Olivier Tastet (ORCID: https://orcid.org/0000-0001-5694-6266)
- Julia Cadrin‐Tourigny (ORCID: https://orcid.org/0000-0002-7489-8744)
- David Busseuil (ORCID: https://orcid.org/0000-0003-1283-403X)
- Paloma Jordà (ORCID: https://orcid.org/0000-0002-9236-1713)
- Robert Avram (ORCID: https://orcid.org/0000-0002-8490-0270)
- Gilbert Jabbour (ORCID: https://orcid.org/0009-0003-5636-2238)
- A. Sowa
- Alexis Nolin-Lapalme
Institutions
- Montreal Heart Institute (CA)
- Health Innovations (United States) (US)
- Mila - Quebec Artificial Intelligence Institute (CA)
- Université de Montréal (CA)
Publication Details
- Journal
- European Heart Journal
- Published
- 2024-09-01
- DOI
- https://doi.org/10.1093/eurheartj/ehae595
- Citations
- 37
- Primary Topic
- Atrial Fibrillation Management and Outcomes
- Type
- article
- Field-Weighted Citation Impact
- 14.88
Funders
- Canada Research Chairs
- Takeda Canada
- Canadian Institutes of Health Research
- Natural Sciences and Engineering Research Council of Canada
- Institute of Genetics