Deep learning-enabled analysis of medical images identifies cardiac sphericity as an early marker of cardiomyopathy and related outcomes.

Journal: Med (New York, N.Y.)
Published Date: Apr 14, 2023

Abstract

BACKGROUND: Quantification of chamber size and systolic function is a fundamental component of cardiac imaging. However, the human heart is a complex structure with significant uncharacterized phenotypic variation beyond traditional metrics of size and function. Examining variation in cardiac shape can add to our ability to understand cardiovascular risk and pathophysiology.

Authors

  • 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.
  • Alan C Kwan
    Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA.
  • Victoria Yuan
    School of Medicine, University of California Los Angeles, Los Angeles, CA 90095, USA.
  • Michael Salerno
    Department of Medicine, University of Virginia, Charlottesville, VA, USA.
  • Daniel C Lee
    Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Illinois.
  • Christine M Albert
    Department of Cardiology, Cedars-Sinai Medical Center, Los Angeles, California.
  • Susan Cheng
    Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA.
  • Debiao Li
  • David Ouyang
    Division of Artificial Intelligence, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, California, USA.
  • Shoa L Clarke
    Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA 94306, USA. Electronic address: shoa@stanford.edu.

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