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Artificial Intelligence in Nuclear Cardiology: Adding Value to Prognostication

  • Cardiac Nuclear Imaging (A Cuocolo and M Petretta, Section Editors)
  • Published:
Current Cardiovascular Imaging Reports Aims and scope Submit manuscript

Abstract

Purpose of the Review

Radionuclide myocardial perfusion imaging (MPI) continues to be an accurate and reproducible method of diagnosing obstructive coronary artery disease (CAD) with predictive, prognostic, and economic value. We review the evolutionary potential of machine learning (ML), a subset of artificial intelligence, as an adjunct to MPI.

Recent Findings

Applying the broad scope of ML, including the integration of deep learning, can leverage the knowledge representation and automated reasoning to detect and extrapolate patterns from high-dimensional features of MPI. There is growing evidence to suggest superior abilities of ML over parametric statistical models for predicting the presence of obstructive CAD, the need for revascularization, and the occurrence of major adverse cardiac events including cardiac death.

Summary

ML is uniquely positioned to provide the next great advancement in the field of nuclear cardiology for improving patient-specific risk stratification.

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Authors and Affiliations

  1. West Virginia University Medicine Heart and Vascular Institute, 1 Medical Center Drive, Morgantown, WV, 26506, USA

    Karthik Seetharam, Sirish Shresthra, James D. Mills & Partho P. Sengupta

Authors
  1. Karthik Seetharam
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  2. Sirish Shresthra
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  3. James D. Mills
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  4. Partho P. Sengupta
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Corresponding author

Correspondence to Partho P. Sengupta.

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Conflict of Interest

Karthik Seetharam declares that he has no conflict of interest.

Sirish Shresthra declares that he has no conflict of interest.

James D. Mills declares that he has no conflict of interest.

Partho P. Sengupta is a consultant for HeartSciences, Ultromics, Kencor Health.

Human and Animal Rights and Informed Consent

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

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This article is part of the Topical Collection on Cardiac Nuclear Imaging

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Seetharam, K., Shresthra, S., Mills, J.D. et al. Artificial Intelligence in Nuclear Cardiology: Adding Value to Prognostication. Curr Cardiovasc Imaging Rep 12, 14 (2019). https://doi.org/10.1007/s12410-019-9490-8

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  • DOI: https://doi.org/10.1007/s12410-019-9490-8

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