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Current Cardiovascular Imaging Reports
Published in: Current Cardiovascular Imaging Reports 5/2019

01-05-2019 | Artificial Intelligence | Cardiac Nuclear Imaging (A Cuocolo and M Petretta, Section Editors)

Artificial Intelligence in Nuclear Cardiology: Adding Value to Prognostication

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

Published in: Current Cardiovascular Imaging Reports | Issue 5/2019

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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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Metadata
Title
Artificial Intelligence in Nuclear Cardiology: Adding Value to Prognostication
Authors
Karthik Seetharam
Sirish Shresthra
James D. Mills
Partho P. Sengupta
Publication date
01-05-2019
Publisher
Springer US
Published in
Current Cardiovascular Imaging Reports / Issue 5/2019
Print ISSN: 1941-9066
Electronic ISSN: 1941-9074
DOI
https://doi.org/10.1007/s12410-019-9490-8

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