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Review
AI in Cardiology
September 20, 2023
Ramsey M.Wehbe,MD, MSAI1,2; Aggelos K.Katsaggelos,PhD3; Kristian J.Hammond,PhD4; et al HaHong,PhD5; Faraz S.Ahmad,MD, MS2,6,9; DavidOuyang,MD7; Sanjiv J.Shah,MD2,9; Patrick M.McCarthy,MD8,9; James D.Thomas,MD2,9
Author Affiliations Article Information
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1Division of Cardiology, Department of Medicine & Biomedical Informatics Center, Medical University of South Carolina, Charleston
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2Division of Cardiology, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois
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3Department of Computer and Electrical Engineering, Northwestern University, Evanston, Illinois
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4Department of Computer Science, Northwestern University, Evanston, Illinois
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5Medtronic, Minneapolis, Minnesota
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6Center for Health Information Partnerships, Institute for Public Health and Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois
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7Division of Cardiology, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, California
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8Division of Cardiac Surgery, Department of Surgery, Northwestern University Feinberg School of Medicine, Chicago, Illinois
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9Center for Artificial Intelligence, Northwestern Medicine Bluhm Cardiovascular Institute, Chicago, Illinois
JAMA Cardiol. 2023;8(11):1089-1098. doi:10.1001/jamacardio.2023.3142
- Correction Error in Author Name
JAMA Cardiology
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Abstract
Importance Artificial intelligence (AI), driven by advances in deep learning (DL), has the potential to reshape the field of cardiovascular imaging (CVI). While DL for CVI is still in its infancy, research is accelerating to aid in the acquisition, processing, and/or interpretation of CVI across various modalities, with several commercial products already in clinical use. It is imperative that cardiovascular imagers are familiar with DL systems, including a basic understanding of how they work, their relative strengths compared with other automated systems, and possible pitfalls in their implementation. The goal of this article is to review the methodology and application of DL to CVI in a simple, digestible fashion toward demystifying this emerging technology.
Observations At its core, DL is simply the application of a series of tunable mathematical operations that translate input data into a desired output. Based on artificial neural networks that are inspired by the human nervous system, there are several types of DL architectures suited to different tasks; convolutional neural networks are particularly adept at extracting valuable information from CVI data. We survey some of the notable applications of DL to tasks across the spectrum of CVI modalities. We also discuss challenges in the development and implementation of DL systems, including avoiding overfitting, preventing systematic bias, improving explainability, and fostering a human-machine partnership. Finally, we conclude with a vision of the future of DL for CVI.
Conclusions and Relevance Deep learning has the potential to meaningfully affect the field of CVI. Rather than a threat, DL could be seen as a partner to cardiovascular imagers in reducing technical burden and improving efficiency and quality of care. High-quality prospective evidence is still needed to demonstrate how the benefits of DL CVI systems may outweigh the risks.
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Artificial Intelligence Radiology Cardiology Cardiac Imaging
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Wehbe RM, Katsaggelos AK, Hammond KJ, et al. Deep Learning for Cardiovascular Imaging: A Review. JAMA Cardiol. 2023;8(11):1089–1098. doi:10.1001/jamacardio.2023.3142
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