Artificial Intelligence and Machine Learning in Arrhythmias and Cardiac Electrophysiology

Author:

Feeny Albert K.1ORCID,Chung Mina K.12ORCID,Madabhushi Anant34,Attia Zachi I.5ORCID,Cikes Maja6ORCID,Firouznia Marjan3,Friedman Paul A.5,Kalscheur Matthew M.78ORCID,Kapa Suraj5ORCID,Narayan Sanjiv M.910ORCID,Noseworthy Peter A.5ORCID,Passman Rod S.11ORCID,Perez Marco V.910ORCID,Peters Nicholas S.12ORCID,Piccini Jonathan P.13ORCID,Tarakji Khaldoun G.2ORCID,Thomas Suma A.2,Trayanova Natalia A.14ORCID,Turakhia Mintu P.91015ORCID,Wang Paul J.910ORCID

Affiliation:

1. Cleveland Clinic Lerner College of Medicine (A.K.F., M.K.C.), Case Western Reserve University, OH.

2. Department of Cardiovascular Medicine, Cleveland Clinic, OH (M.K.C., K.G.T., S.A.T.).

3. Department of Biomedical Engineering (A.M., M.F.), Case Western Reserve University, OH.

4. Louis Stokes Cleveland Veterans Affairs Medical Center, Cleveland, OH (A.M.).

5. Department of Cardiovascular Medicine, Mayo Clinic College of Medicine, Rochester, MN (Z.I.A., P.A.F., S.K., P.A.N., ).

6. Department of Cardiovascular Diseases, University of Zagreb School of Medicine & University Hospital Center Zagreb, Croatia (M.C.).

7. Division of Cardiovascular Medicine, Department of Medicine, School of Medicine & Public Health, University of Wisconsin (M.M.K.).

8. William S. Middleton Veterans Hospital, Madison, WI (M.M.K.).

9. Division of Cardiovascular Medicine, Stanford University, CA (S.M.N., M.V.P., M.P.T., P.J.W.).

10. Veterans Affairs Palo Alto Health Care System, CA (S.M.N., M.V.P., M.P.T., P.J.W.).

11. Division of Cardiology, Northwestern University, Feinberg School of Medicine, Chicago, IL (R.S.P.).

12. National Heart Lung Institute & Centre for Cardiac Engineering, Imperial College London, United Kingdom (N.S.P.).

13. Duke Clinical Research Institute, Duke University Medical Center, Durham, NC (J.P.P.).

14. Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD (N.A.T.).

15. Center for Digital Health, Stanford University School of Medicine, CA (M.P.T.).

Abstract

Artificial intelligence (AI) and machine learning (ML) in medicine are currently areas of intense exploration, showing potential to automate human tasks and even perform tasks beyond human capabilities. Literacy and understanding of AI/ML methods are becoming increasingly important to researchers and clinicians. The first objective of this review is to provide the novice reader with literacy of AI/ML methods and provide a foundation for how one might conduct an ML study. We provide a technical overview of some of the most commonly used terms, techniques, and challenges in AI/ML studies, with reference to recent studies in cardiac electrophysiology to illustrate key points. The second objective of this review is to use examples from recent literature to discuss how AI and ML are changing clinical practice and research in cardiac electrophysiology, with emphasis on disease detection and diagnosis, prediction of patient outcomes, and novel characterization of disease. The final objective is to highlight important considerations and challenges for appropriate validation, adoption, and deployment of AI technologies into clinical practice.

Publisher

Ovid Technologies (Wolters Kluwer Health)

Subject

Physiology (medical),Cardiology and Cardiovascular Medicine

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