Deep Learning of Electrocardiograms in Sinus Rhythm From US Veterans to Predict Atrial Fibrillation.

Journal: JAMA cardiology
PMID: 37851434

Abstract

IMPORTANCE: 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.

Authors

  • Neal Yuan
    Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
  • Grant Duffy
    Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, 127 S San Vicente Blvd A3600, Los Angeles, CA 90048, United States.
  • Sanket S Dhruva
    Robert Wood Johnson Foundation Clinical Scholars Program, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut, United States of America.
  • Adam Oesterle
    Department of Medicine, University of California, San Francisco.
  • Cara N Pellegrini
    Department of Medicine, University of California, San Francisco.
  • John Theurer
    Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, 90048.
  • Marzieh Vali
    San Francisco VA Medical Center, 4150 Clement Street, San Francisco, 94121, CA, USA.
  • Paul A Heidenreich
    Veterans Administration Palo Alto Health Care System, Palo Alto, California.
  • Salomeh Keyhani
    San Francisco Veteran Affair Health Care System, San Francisco, CA USA.
  • David Ouyang
    Division of Artificial Intelligence, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, California, USA.

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