Performance of a Convolutional Neural Network and Explainability Technique for 12-Lead Electrocardiogram Interpretation.

Journal: JAMA cardiology
Published Date: Nov 1, 2021

Abstract

IMPORTANCE: Millions of clinicians rely daily on automated preliminary electrocardiogram (ECG) interpretation. Critical comparisons of machine learning-based automated analysis against clinically accepted standards of care are lacking.

Authors

  • J Weston Hughes
    Department of Computer Science, Stanford University, Palo Alto, CA 94025.
  • Jeffrey E Olgin
    Division of Cardiology, Hypertrophic Cardiomyopathy Center of Excellence, University of California San Francisco, San Francisco, California.
  • Robert Avram
    Division of Cardiology, Department of Medicine, Montreal Heart Institute, University of Montreal, Montreal, QC H1T 1C8, Canada. Electronic address: robert.avram.md@gmail.com.
  • Sean A Abreau
    Department of Medicine, Division of Cardiology, University of California, San Francisco, San Francisco.
  • Taylor Sittler
    Department of Laboratory Medicine, University of California, San Francisco, San Francisco.
  • Kaahan Radia
    RISE Lab, Department of Electrical Engineering and Computer Science, University of California, Berkeley, Berkeley.
  • Henry Hsia
    Department of Medicine, Division of Cardiology, University of California, San Francisco, San Francisco.
  • Tomos Walters
    Department of Medicine, Division of Cardiology, University of California, San Francisco, San Francisco.
  • Byron Lee
    Department of Medicine, Division of Cardiology, University of California, San Francisco, San Francisco.
  • Joseph E Gonzalez
    RISE Lab, Department of Electrical Engineering and Computer Science, University of California, Berkeley, Berkeley.
  • Geoffrey H Tison
    Department of Medicine (G.H.T., M.H.L., E.F., M.A.A., C.J., K.E.F., R.C.D.).

Keywords

External Resources

Popular Topics
Recent Journals