Integrated prediction of lesion-specific ischaemia from quantitative coronary CT angiography using machine learning: a multicentre study.

Abstract

Authors

• 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.
• Sara Gaur
  Department of Cardiology, Aarhus University Hospital, Aarhus, Denmark.
• Kristian A Ovrehus
  Department of Cardiology, Aarhus University Hospital, Aarhus, Denmark.
• Piotr J Slomka
  Department of Imaging, Medicine, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California Piotr.Slomka@cshs.org.
• Julian Betancur
  Department of Imaging, Medicine, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California.
• Markus Goeller
  Biomedical Imaging Research Institute, Department of Biomedical Sciences, Cedars-Sinai Medical Center, Taper building, A238, 8700 Beverly Blvd, Los Angeles, 90048, USA.
• Michaela M Hell
  Department of Cardiology, Friedrich-Alexander Universitat Erlangen-Nurnberg, Erlangen, Germany.
• Heidi Gransar
  Departments of Imaging, Medicine, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California.
• Daniel S Berman
  Departments of Imaging and Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
• Stephan Achenbach
  Department of Cardiology, Friedrich-Alexander Universitat Erlangen-Nurnberg, Erlangen, Germany.
• Hans Erik Botker
  Department of Cardiology, Aarhus University Hospital, Aarhus, Denmark.
• Jesper Moller Jensen
  Department of Cardiology, Aarhus University Hospital, Aarhus, Denmark.
• Jens Flensted Lassen
  Department of Cardiology, Aarhus University Hospital, Aarhus, Denmark.
• Bjarne Linde Norgaard
  Department of Cardiology, Aarhus University Hospital, Aarhus, Denmark.

Keywords

External Resources