Author + information
- Received October 31, 2018
- Accepted December 13, 2018
- Published online March 18, 2019.
- Damini Dey, PhDa,
- Piotr J. Slomka, PhDa,
- Paul Leeson, MBBChir, PhDb,
- Dorin Comaniciu, PhDc,
- Sirish Shrestha, MSd,
- Partho P. Sengupta, MDd and
- Thomas H. Marwick, MBBS, PhD, MPHe,∗ (, )@tom_marwick@BakerResearchAu
- aDepartments of Biomedical Sciences and Medicine, Cedars-Sinai Medical Center, Biomedical Imaging Research Institute, Los Angeles, California
- bOxford Cardiovascular Clinical Research Facility, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom
- cSiemens Corporate Technology, Munich, Germany
- dSection of Cardiology, West Virginia University, Morgantown, West Virginia
- eBaker Heart and Diabetes Research Institute, Melbourne, Australia
- ↵∗Address for correspondence:
Dr. Thomas H. Marwick, Baker Heart and Diabetes Institute, 75 Commercial Road, Melbourne, Victoria 3004, Australia.
• Problems with timing, efficiency, and missed diagnoses occur at all stages of the imaging chain. The application of AI may reduce cost and improve value at all stages of image acquisition, interpretation, and decision-making.
• The main fields of AI for imaging will pertain to disease phenotyping, diagnostic support, and image interpretation. Grouping of relevant clinical and imaging information with cluster analysis may provide opportunities to better characterize disease. Diagnostic support will be provided by automated image segmentation and automated measurements. The initial steps are being taken towards automated image acquisition and analysis.
• “Big data” from imaging will interface with high volumes of data from the electronic health record and pathology to provide new insights and opportunities to personalize therapy.
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.
This study was supported in part by a Partnership Grant from the National Health and Medical Research Council and from National Heart, Lung, and Blood Institute grant 1R01HL133616. Dr. Dey has received software royalties from Cedars-Sinai Medical Center and has a patent. Dr. Slomka has received a research grant from Siemens Medical Solutions; and has received software royalties from Cedars Sinai. Dr. Leeson is a founder, stockholder, and non-executive director of Ultromics Ltd.; and has received a research grant from Lantheus Medical Imaging. Dr. Comaniciu is a salaried employee of Siemens Healthineers. Dr. Marwick has received research grant support from GE Medical Systems for the SUCCOUR study. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose. Patrick W Serruys, M.D., Ph.D. served as Guest Associate Editor for this paper.
Listen to this manuscript's audio summary by Editor-in-Chief Dr. Valentin Fuster on JACC.org.
- Received October 31, 2018.
- Accepted December 13, 2018.
- 2019 American College of Cardiology Foundation
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