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Machine Meets Biology: a Primer on Artificial Intelligence in Cardiology and Cardiac Imaging

Current Cardiology Reports volume 20, Article number: 139 (2018) Cite this article

Abstract

Purpose of Review

An understanding of the basics concepts of deep learning can be helpful in not only understanding the potential applications of this technique but also in critically reviewing literature in which neural networks are utilized for analysis and modeling.

Recent Findings

The term “deep learning” has been applied to a subset of machine learning that utilizes a “neural network” and is often used interchangeably with “artificial intelligence.” It has been increasingly utilized in healthcare for computational “learning”, especially for pattern recognition for diagnostic imaging. Another promising application is the potential for these neural networks to improve the accuracy in the identification of patients who are at risk for cardiovascular events and could benefit most from preventive treatment in comparison with more conventional statistical techniques. The importance of such tailored cardiovascular risk assessment and disease management in individual patients is far reaching given that cardiovascular disease is the leading cause of morbidity and mortality in the world. Nearly half of myocardial infarctions and strokes occur in patients who are not predicted to be at risk for cardiovascular events by current guideline-based approaches. Equally important are individuals who are not at risk for cardiovascular events and yet are given expensive and unnecessary preventive treatment with potential untoward side effects.

Summary

The application of powerful artificial intelligence/deep learning tools in medicine is likely to result in more effective and efficient health care delivery with the potential for significant cost savings by shifting preventative treatment from inappropriate to appropriate patient subgroups.

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Author information

Affiliations

  1. University of Maryland School of Medicine, Baltimore, MD, USA

    Matthew E. Dilsizian & Eliot L. Siegel

  2. Imaging Services, VA Maryland Health Care System, 10 North, Greene Street, Mail Code 114, Baltimore, MD, 21201, USA

    Eliot L. Siegel

Authors
  1. Matthew E. Dilsizian
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  2. Eliot L. Siegel
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Corresponding author

Correspondence to Eliot L. Siegel.

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The authors declare that they have no conflict of interest.

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This article does not contain any studies with human or animal subjects performed by any of the authors.

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This article is part of the Topical Collection on Nuclear Cardiology

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Dilsizian, M.E., Siegel, E.L. Machine Meets Biology: a Primer on Artificial Intelligence in Cardiology and Cardiac Imaging. Curr Cardiol Rep 20, 139 (2018). https://doi.org/10.1007/s11886-018-1074-8

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  • DOI: https://doi.org/10.1007/s11886-018-1074-8

Keywords

  • Artificial intelligence
  • Neural networks
  • Machine learning
  • Convolutional neural networks
  • Big data
  • Electronic health records
  • Cardiac imaging