Prediction of revascularization after myocardial perfusion SPECT by machine learning in a large population.

Journal: Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology
PMID: 25480110

Abstract

OBJECTIVE: We aimed to investigate if early revascularization in patients with suspected coronary artery disease can be effectively predicted by integrating clinical data and quantitative image features derived from perfusion SPECT (MPS) by machine learning (ML) approach.

Authors

  • Reza Arsanjani
    Department of Cardiovascular Diseases, Mayo Clinic, Scottsdale, AZ.
  • Damini Dey
    Departments of Imaging and Medicine, and Cedars-Sinai Heart Institute, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Taper A238, Los Angeles, CA, 90048, USA.
  • Tigran Khachatryan
    Departments of Imaging and Medicine, and Cedars-Sinai Heart Institute, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Taper A238, Los Angeles, CA, 90048, USA.
  • Aryeh Shalev
    Departments of Imaging and Medicine, and Cedars-Sinai Heart Institute, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Taper A238, Los Angeles, CA, 90048, USA.
  • Sean W Hayes
    Departments of Imaging and Medicine, and Cedars-Sinai Heart Institute, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Taper A238, Los Angeles, CA, 90048, USA.
  • Mathews Fish
    Oregon Heart and Vascular Institute, Sacred Heart Medical Center, Springfield, OR, USA.
  • Rine Nakanishi
    Departments of Imaging and Medicine, and Cedars-Sinai Heart Institute, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Taper A238, Los Angeles, CA, 90048, USA.
  • Guido Germano
    Departments of Imaging and Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
  • Daniel S Berman
    Departments of Imaging and Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
  • Piotr Slomka
    Departments of Imaging and Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA.

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