Deep Learning for Explainable Estimation of Mortality Risk From Myocardial Positron Emission Tomography Images.

Journal: Circulation. Cardiovascular imaging
Published Date:

Abstract

BACKGROUND: We aim to develop an explainable deep learning (DL) network for the prediction of all-cause mortality directly from positron emission tomography myocardial perfusion imaging flow and perfusion polar map data and evaluate it using prospective testing.

Authors

  • Ananya Singh
    Departments of Imaging, Medicine and Biomedical Sciences, Division of Artificial Intelligence in Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA. Ananya.Singh@cshs.org.
  • Jacek Kwiecinski
    Division of Artificial Intelligence in Medicine, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, California.
  • Robert J H Miller
    Department of Imaging and Medicine and the Smidt Heart Institute (E.E., S.C., H.G., S.C., R.J.H.M., P.J.S., B.K.T., D.S.B.), Cedars-Sinai Medical Center, Los Angeles, CA.
  • Yuka Otaki
    Department of Imaging, Medicine, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California.
  • Paul B Kavanagh
    Departments of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences and Imaging, Cedars-Sinai Medical Center, Los Angeles, California, USA.
  • Serge D Van Kriekinge
    Departments of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences and Imaging, Cedars-Sinai Medical Center, Los Angeles, California, USA.
  • Tejas Parekh
    Division of Artificial Intelligence in Medicine, Department of Medicine, Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California.
  • Heidi Gransar
    Departments of Imaging, Medicine, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California.
  • Konrad Pieszko
    Department of Cardiology, Nowa Sól Multidisciplinary Hospital, Nowa Sól, Poland.
  • Aditya Killekar
    Division of Artificial Intelligence, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
  • Ramyashree Tummala
    Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA (A.S., J.K., R.J.H.M., Y.O., P.B.K., S.D.V.K., T.P., H.G., K.P., A.K., R.T., J.X.L., D.S.B., D.D., P.J.S.).
  • Joanna X Liang
    Division of Nuclear Medicine, Department of Imaging, and Departments of Medicine and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California.
  • Marcelo F Di Carli
    Cardiovascular Division, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.
  • Daniel S Berman
    Departments of Imaging and Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
  • 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.
  • Piotr J Slomka
    Department of Imaging, Medicine, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California Piotr.Slomka@cshs.org.