Using Deep learning to Predict Cardiovascular Magnetic Resonance Findings from Echocardiography Videos.

Journal: Journal of the American Society of Echocardiography : official publication of the American Society of Echocardiography
Published Date: May 30, 2025

Abstract

BACKGROUND: Echocardiography is the most common modality for assessing cardiac structure and function. While cardiac magnetic resonance (CMR) imaging is less accessible, CMR can provide unique tissue characterization including late gadolinium enhancement (LGE), T1 and T2 mapping, and extracellular volume (ECV) which are associated with tissue fibrosis, infiltration, and inflammation. Deep learning has been shown to uncover findings not recognized by clinicians, however it is unknown whether CMR-based tissue characteristics can be derived from echocardiography videos using deep learning.

Authors

  • Yuki Sahashi
    Department of Cardiovascular Medicine, Gifu Heart Center, Gifu, Japan.
  • 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.
  • Grant Duffy
    Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, 127 S San Vicente Blvd A3600, Los Angeles, CA 90048, United States.
  • Debiao Li
  • Susan Cheng
    Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA.
  • Daniel S Berman
    Departments of Imaging and Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
  • David Ouyang
    Division of Artificial Intelligence, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, California, USA.
  • Alan C Kwan
    Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA.

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