The Lead Episode 40: A Discussion of Deep Learning of Electrocardiograms in Sinus Rhythm From US Veterans to Predict Atrial Fibrillation-The Lead Episode 40 Speaker Information

The Lead Episode 40 Speaker Information

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In this discussion, the topic of deep learning of electrocardiograms (ECGs) in sinus rhythm from US veterans to predict atrial fibrillation is explored. The authors of the article being discussed are Neal Yuan, MD, Grant Duffy, BS, Sanket S. Dhruva, MD, MHS, Adam Oesterle, MD, Cara N. Pellegrini, MD, John Theurer, BS, Marzieh Vali, MS, Paul A. Heidenreich, MD, Salomeh Keyhani, MD, and David Ouyang, MD. The article was published in JAMA Cardiology. <br /><br />The host of the discussion is William H. Sauer, MD, FHRS, CCDS from Brigham and Women's Hospital. The contributors to the discussion are Jagmeet P. Singh, MD, PhD, FHRS from Massachusetts General Hospital and Michael A. Rosenberg, MD, FHRS from the University of Colorado Anschutz. <br /><br />During the discussion, the contributors disclose any relevant relationships or conflicts of interest. Michael A. Rosenberg has disclosed that he has received honoraria/speaking/consulting fees from various companies but has nothing else to disclose. William H. Sauer has disclosed honoraria/speaking/consulting fees from Biotronik, Biosense Webster, Inc., Abbott, and Boston Scientific, as well as research funding from Medtronic. Jagmeet P. Singh has disclosed honoraria/speaking/consulting fees from multiple companies including Medtronic, Boston Scientific, and Biotronik, among others. <br /><br />Overall, the discussion explores the topic of deep learning of ECGs in sinus rhythm as a predictive tool for atrial fibrillation. The participants in the discussion provide their expertise and insights on the subject matter.

Keywords

deep learning

electrocardiograms

sinus rhythm

atrial fibrillation

US veterans

JAMA Cardiology

William H. Sauer

Jagmeet P. Singh

Michael A. Rosenberg

predictive tool