Artificial Intelligence in Cardiovascular Imaging: JACC State-of-the-Art Review.

Journal: Journal of the American College of Cardiology
Published Date:

Abstract

Data science is likely to lead to major changes in cardiovascular imaging. Problems with timing, efficiency, and missed diagnoses occur at all stages of the imaging chain. The application of artificial intelligence (AI) is dependent on robust data; the application of appropriate computational approaches and tools; and validation of its clinical application to image segmentation, automated measurements, and eventually, automated diagnosis. AI may reduce cost and improve value at the stages of image acquisition, interpretation, and decision-making. Moreover, the precision now possible with cardiovascular imaging, combined with "big data" from the electronic health record and pathology, is likely to better characterize disease and personalize therapy. This review summarizes recent promising applications of AI in cardiology and cardiac imaging, which potentially add value to patient care.

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.
  • Piotr J Slomka
    Department of Imaging, Medicine, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California Piotr.Slomka@cshs.org.
  • Paul Leeson
    Ultromics Ltd, Oxford, United Kingdom.
  • Dorin Comaniciu
  • Sirish Shrestha
    Division of Cardiology, WVU Heart & Vascular Institute, West Virginia University, Morgantown, West Virginia.
  • Partho P Sengupta
    Division of Cardiovascular Diseases and Hypertension, Robert Wood Johnson University Hospital, and Rutgers Robert Wood Johnson Medical School, New Brunswick, New Jersey, USA.
  • Thomas H Marwick
    Menzies Institute for Medical Research, University of Tasmania, Hobart, Australia; Baker Heart and Diabetes Institute, Melbourne, Australia.