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Heart Rhythm Society

  • Atrial Fibrillation
  • Electrocardiography

The Lead Episode 40: A Discussion of Deep Learning of Electrocardiograms in Sinus Rhythm From US 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.

Credit & MOC Details

Credit information is provided by the course provider. We recommend confirming credit eligibility with the provider or your licensing board before starting the activity.

Credit types

AMA PRA Category 1 Credit™Up to 0.25 credits

MOC types

ABIM — Medical KnowledgeUp to 0.25 pointsABP — Lifelong Learning and Self-AssessmentUp to 0.25 points

FDA REMS

No

Qualifies for MIPS

No

Pricing

Free

Reported to ACCME by the provider

  • On-demand / recorded video
  • Credit · Up to 0.25 credits
  • MOC · Up to 0.25 points
  • Released
    Dec 8, 2023
  • Last day to claim credit
    Dec 9, 2026 8 weeks left