Prospective validation of a deep learning electrocardiogram algorithm for the detection of left ventricular systolic dysfunction.

Journal: Journal of cardiovascular electrophysiology
Published Date: Mar 10, 2019

Abstract

OBJECTIVES: We sought to validate a deep learning algorithm designed to predict an ejection fraction (EF) less than or equal to 35% based on the 12-lead electrocardiogram (ECG) in a large prospective cohort.

Authors

  • Zachi I Attia
    Department of Cardiovascular Medicine, Mayo Clinic College of Medicine, Rochester, Minnesota, USA.
  • Suraj Kapa
    Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA.
  • Xiaoxi Yao
    Department of Health Sciences Research, Division of Health Care Policy and Research, Mayo Clinic, Rochester, Minnesota.
  • Francisco Lopez-Jimenez
    Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USA.
  • Tarun L Mohan
    Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota.
  • Patricia A Pellikka
    Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USA.
  • Rickey E Carter
    Department of Health Sciences Research, Mayo Clinic, Jacksonville, Florida.
  • Nilay D Shah
    Department of Health Sciences Research, Mayo Clinic, Rochester, MN, United States.
  • Paul A Friedman
    Department of Cardiovascular Medicine, Mayo Clinic College of Medicine, Rochester, Minnesota, USA.
  • Peter A Noseworthy
    Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota.

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