Automated vessel-specific coronary artery calcification quantification with deep learning in a large multi-centre registry.

Journal: European heart journal. Cardiovascular Imaging
Published Date: Jun 28, 2024

Abstract

AIMS: Vessel-specific coronary artery calcification (CAC) is additive to global CAC for prognostic assessment. We assessed accuracy and prognostic implications of vessel-specific automated deep learning (DL) CAC analysis on electrocardiogram (ECG) gated and attenuation correction (AC) computed tomography (CT) in a large multi-centre registry.

Authors

  • Michelle C Williams
    British Heart Foundation Centre for Cardiovascular Science, University of Edinburgh, Chancellor's Building, 49 Little France Cres, Edinburgh, UK.
  • Aakash D Shanbhag
    Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California.
  • Jianhang Zhou
    PAMI Research Group, Dept. of Computer and Information Science, University of Macau, Taipa, Macau, China; Shenzhen Research Institute of Big Data, Shenzhen, 518172, China. Electronic address: yc07424@um.edu.mo.
  • Anna M Michalowska
    Departments of Medicine (Division of Artificial Intelligence), Imaging and Biomedical Sciences Cedars-Sinai Medical Center, 6500 Wilshire Blvd, Floor 4, Los Angeles 90048 CA, USA.
  • Mark Lemley
    Oregon Heart and Vascular Institute, Sacred Heart Medical Center, Springfield, Oregon.
  • 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.
  • Aditya Killekar
    Division of Artificial Intelligence, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
  • Parker Waechter
    Departments of Medicine (Division of Artificial Intelligence), Imaging and Biomedical Sciences Cedars-Sinai Medical Center, 6500 Wilshire Blvd, Floor 4, Los Angeles 90048 CA, USA.
  • Heidi Gransar
    Departments of Imaging, Medicine, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California.
  • 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.
  • Valerie Builoff
    Department of Medicine (Division of Artificial Intelligence in Medicine), Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California, USA.
  • Attila Feher
    Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, United States.
  • Edward J Miller
    Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, United States.
  • Timothy Bateman
    Cardiovascular Imaging Technologies, Kansas City, Missouri, 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.
  • Daniel Berman
    Department of Imaging, Cedars-Sinai Medical Center, Cedars-Sinai Heart Institute, Los Angeles, California.
  • Piotr J Slomka
    Department of Imaging, Medicine, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California Piotr.Slomka@cshs.org.

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