Machine and deep learning models for accurate detection of ischemia and scar with myocardial blood flow positron emission tomography imaging.

Journal: Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology
PMID: 38185409

Abstract

BACKGROUND: Quantification of myocardial blood flow (MBF) is used for the noninvasive diagnosis of patients with coronary artery disease (CAD). This study compared traditional statistics, machine learning, and deep learning techniques in their ability to diagnose disease using only the rest and stress MBF values.

Authors

  • Daniel Berman
    Department of Imaging, Cedars-Sinai Medical Center, Cedars-Sinai Heart Institute, Los Angeles, California.
  • Chad Hunter
    University of Ottawa Heart Institute, 40 Ruskin Street, Ottawa, K1Y 4W7, Canada.
  • Alomgir Hossain
    Department of Genetic Engineering and Biotechnology, University of Rajshahi, Rajshahi, 6205, Bangladesh. Electronic address: s1910861106@ru.ac.bd.
  • Jason Yao
    Department of Radiology, University of Ottawa Faculty of Medicine, 501 Smyth Road, Box 232, Ottawa, ON, K1H 8L6, Canada. jason.yao21@gmail.com.
  • Emily Workman
    The MITRE Corporation, 7515 Colshire Drive, McLean, VA 22102, USA.
  • Steven Guan
    The MITRE Corporation, 7515 Colshire Drive, McLean, VA 22102, USA.
  • Laura Strickhart
    The MITRE Corporation, 7515 Colshire Drive, McLean, VA 22102, USA.
  • Rob Beanlands
    Division of Cardiology, University of Ottawa Heart Institute, Ottawa, Ontario, Canada.
  • David Slater
    The MITRE Corporation, 7515 Colshire Drive, McLean, VA 22102, USA.
  • Robert A deKemp
    University of Ottawa Heart Institute, 40 Ruskin Street, Ottawa, K1Y 4W7, Canada. Electronic address: radekemp@ottawaheart.ca.

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