High-Throughput Deep Learning Detection of Mitral Regurgitation.

Journal: Circulation
PMID: 39129623

Abstract

BACKGROUND: Diagnosis of mitral regurgitation (MR) requires careful evaluation by echocardiography with Doppler imaging. This study presents the development and validation of a fully automated deep learning pipeline for identifying apical 4-chamber view videos with color Doppler echocardiography and detecting clinically significant (moderate or severe) MR from transthoracic echocardiograms.

Authors

  • Amey Vrudhula
    Icahn School of Medicine at Mount Sinai, New York (A.V.).
  • Grant Duffy
    Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, 127 S San Vicente Blvd A3600, Los Angeles, CA 90048, United States.
  • Milos Vukadinovic
    Department of Bioengineering, University of California Los Angeles, Los Angeles, CA 90095, USA; Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA; Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA.
  • David Liang
  • Susan Cheng
    Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA.
  • David Ouyang
    Division of Artificial Intelligence, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, California, USA.

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