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J Am Coll Cardiol. 2019 Mar 26;73(11):1317-1335. doi: 10.1016/j.jacc.2018.12.054.

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

Author information

1
Departments of Biomedical Sciences and Medicine, Cedars-Sinai Medical Center, Biomedical Imaging Research Institute, Los Angeles, California.
2
Oxford Cardiovascular Clinical Research Facility, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom.
3
Siemens Corporate Technology, Munich, Germany.
4
Section of Cardiology, West Virginia University, Morgantown, West Virginia.
5
Baker Heart and Diabetes Research Institute, Melbourne, Australia. Electronic address: Tom.Marwick@bakeridi.edu.au.

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.

KEYWORDS:

artificial intelligence; cardiovascular imaging; deep learning; machine learning

PMID:
30898208
DOI:
10.1016/j.jacc.2018.12.054

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