Activity ID
11944Expires
September 20, 2026Format Type
Journal-basedCME Credit
1Fee
$30CME Provider: JAMA Cardiology
Description of CME Course
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.
Disclaimers
1. This activity is accredited by the American Medical Association.
2. This activity is free to AMA members.
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NoNOTE: If a Member Board has not deemed this activity for MOC approval as an accredited CME activity, this activity may count toward an ABMS Member Board’s general CME requirement. Please refer directly to your Member Board’s MOC Part II Lifelong Learning and Self-Assessment Program Requirements.
Educational Objectives
To identify the key insights or developments described in this article
Keywords
Cardiac Imaging, Cardiology, Health Informatics
Competencies
Medical Knowledge
CME Credit Type
AMA PRA Category 1 Credit
DOI
10.1001/jamacardio.2023.3142