The Lead Episode 40: A Discussion of Deep Learning of Electrocardiograms in Sinus Rhythm from U.S. Veterans to Predict Atrial Fibrillation

Discussion Topic: Deep Learning of Electrocardiograms in Sinus Rhythm from U.S. Veterans to Predict Atrial Fibrillation

William H. Sauer, MD, FHRS, CCDS, of Brigham and Women’s Hospital is joined by guests Michael A. Rosenberg, MD, FHRS, of University of Colorado Anschutz, and Jagmeet P. Singh, MD, PhD, FHRS, of Massachusetts General Hospital to discuss early detection of atrial fibrillation (AF) may help prevent adverse cardiovascular events such as stroke. Deep learning applied to electrocardiograms (ECGs) has been successfully used for early identification of several cardiovascular diseases. Deep learning of outpatient sinus rhythm ECGs predicted AF within 31 days in populations with diverse demographics and comorbidities. Similar models could be used in future AF screening efforts to reduce adverse complications associated with this disease.

Host: William H. Sauer, MD, FHRS, CCDS
Guests: Michael A. Rosenberg, MD, FHRS and Jagmeet P. Singh, MD, PhD, FHRS

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