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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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.
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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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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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DOI: https://doi.org/10.1007/s12170-020-00649-1