ECG-AI AF
Gilbert Jabbour, Alexis Nolin-Lapalme, Olivier Tastet, Denis Corbin, Paloma Jordà, Achille Sowa, Jacques Delfrate, David Busseuil, Julie Hussin, Marie-Pierre Dubé, Jean-Claude Tardif, Léna Rivard, Laurent Macle, Julia Cadrin-Tourigny, Paul Khairy, Robert Avram*, Rafik Tadros*
Atrial fibrillation is the most common sustained cardiac arrhythmia in adults and is associated with an increased risk of stroke, heart failure, cognitive decline, hospitalisations, and death. Deep learning applied to electrocardiograms (ECG-AI) is an emerging approach for predicting atrial fibrillation or flutter (AF). We introduce an open-weights ECG-AI model developed at the Montreal Heart Institute (MHI) and externally validated using MIMIC-IV dataset, comparing its performance with clinical and AF polygenic scores (PGS).
August 30, 2024
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