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Artificial Intelligence Applications to Improve Risk Prediction Tools in Electrophysiology

  • Arrhythmias (J. Bunch, Section Editor)
  • Published:
Current Cardiovascular Risk Reports Aims and scope Submit manuscript

Abstract

Purpose of Review

Artificial intelligence (AI) is an aspect of computer technology that imitates the ability of the human mind to analyze data. Over the last few years, there has been a paradigm shift in the utilization of AI in clinical practice. It is imperative for the clinical electrophysiologist to understand the basics of AI, and its potential applications in the field as new applications are developed and implemented.

Recent Findings

Multiple investigators have demonstrated various AI algorithms that can be utilized in clinical care. These include applications such as electronic stethoscopes and electrocardiographic prediction of atrial fibrillation or congestive heart failure. AI may also be used in cardiovascular imaging, to identify disease patterns and even compose preliminary reports.

Summary

Herein, we seek to familiarize readers with terms associated with AI, such as machine learning and neural networks. Further, we review the applications of AI in bedside clinical calculators, electrocardiography, and the field of cardiovascular imaging. A critical appraisal of AI is provided with specific review of hurdles in the integration of AI in clinical practice.

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Abbreviations

AI:

Artificial intelligence

ML:

Machine learning.

ECG:

Electrocardiogram

SCD:

Sudden cardiac death

HCM:

Hypertrophic cardiomyopathy

CT:

Computed tomography

MRI:

Magnetic resonance imaging

CNN:

Convolutional neural network

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Acknowledgments

We would like to acknowledge the help of I. Zachi Attia, MS (Department of Cardiovascular Medicine, Mayo Clinic College of Medicine, Rochester, MN, USA) in technical advice related to the manuscript.

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Authors and Affiliations

  1. Department of Cardiovascular Medicine, Mayo Clinic College of Medicine, 200 First Street SW, Rochester, MN, 55905, USA

    Gurukripa N. Kowlgi, Fatima M. Ezzeddine & Suraj Kapa

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  1. Gurukripa N. Kowlgi
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  2. Fatima M. Ezzeddine
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  3. Suraj Kapa
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Contributions

First author (G.N.K.) contributed by performing a literature review, drafting the manuscript, and making revisions. F.E. assisted in drafting specific sections of the document. Senior author (S.K.) helped with manuscript revisions and mentorship.

Corresponding author

Correspondence to Suraj Kapa.

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The authors declare that they have no conflicts of interest. Suraj Kapa has IP over AI algorithms to identify age, cardiac function, and sex from ECGs.

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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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Kowlgi, G.N., Ezzeddine, F.M. & Kapa, S. Artificial Intelligence Applications to Improve Risk Prediction Tools in Electrophysiology. Curr Cardiovasc Risk Rep 14, 13 (2020). https://doi.org/10.1007/s12170-020-00649-1

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