Emerging role of artificial intelligence in cardiac electrophysiology

Rajesh Kabra; Sharat Israni; Bharat Vijay; Chaitanya Baru; Raghuveer Mendu; Mark Fellman; Arun Sridhar; Pamela Mason; Jim W. Cheung; Luigi DiBiase; Srijoy Mahapatra; Jerome Kalifa; Steven A. Lubitz; Peter A. Noseworthy; Rachita Navara; David D. McManus; Mitchell Cohen; Mina K. Chung; Natalia Trayanova; Rakesh Gopinathannair; Dhanunjaya Lakkireddy

doi:10.1016/j.cvdhj.2022.09.001

Emerging role of artificial intelligence in cardiac electrophysiology

Rajesh Kabra
, Sharat Israni
, Bharat Vijay
, Chaitanya Baru
, Raghuveer Mendu
, Mark Fellman
, Arun Sridhar
, Pamela Mason
, Jim W. Cheung
, Luigi DiBiase
, Srijoy Mahapatra
, Jerome Kalifa
, Steven A. Lubitz
, Peter A. Noseworthy
, Rachita Navara
, David D. McManus
, Mitchell Cohen
, Mina K. Chung
, Natalia Trayanova
, Rakesh Gopinathannair

Dhanunjaya Lakkireddy

Whiting School of Engineering

Research output: Contribution to journal › Review article › peer-review

Abstract

Artificial intelligence (AI) and machine learning (ML) have significantly impacted the field of cardiovascular medicine, especially cardiac electrophysiology (EP), on multiple fronts. The goal of this review is to familiarize readers with the field of AI and ML and their emerging role in EP. The current review is divided into 3 sections. In the first section, we discuss the definitions and basics of AI, ML, and big data. In the second section, we discuss their application to EP in the context of detection, prediction, and management of arrhythmias. Finally, we discuss the regulatory issues, challenges, and future directions of AI in EP.

Original language	English (US)
Pages (from-to)	263-275
Number of pages	13
Journal	Cardiovascular Digital Health Journal
Volume	3
Issue number	6
DOIs	https://doi.org/10.1016/j.cvdhj.2022.09.001
State	Published - Dec 2022

Keywords

Artificial intelligence
Big data
Cardiac electrophysiology
Computational modeling
Deep learning
Machine learning

ASJC Scopus subject areas

Biomedical Engineering
Critical Care and Intensive Care Medicine
Cardiology and Cardiovascular Medicine

Access to Document

10.1016/j.cvdhj.2022.09.001

Cite this

Kabra, R., Israni, S., Vijay, B., Baru, C., Mendu, R., Fellman, M., Sridhar, A., Mason, P., Cheung, J. W., DiBiase, L., Mahapatra, S., Kalifa, J., Lubitz, S. A., Noseworthy, P. A., Navara, R., McManus, D. D., Cohen, M., Chung, M. K., Trayanova, N., ... Lakkireddy, D. (2022). Emerging role of artificial intelligence in cardiac electrophysiology. Cardiovascular Digital Health Journal, 3(6), 263-275. https://doi.org/10.1016/j.cvdhj.2022.09.001

Kabra, R, Israni, S, Vijay, B, Baru, C, Mendu, R, Fellman, M, Sridhar, A, Mason, P, Cheung, JW, DiBiase, L, Mahapatra, S, Kalifa, J, Lubitz, SA, Noseworthy, PA, Navara, R, McManus, DD, Cohen, M, Chung, MK, Trayanova, N, Gopinathannair, R & Lakkireddy, D 2022, 'Emerging role of artificial intelligence in cardiac electrophysiology', Cardiovascular Digital Health Journal, vol. 3, no. 6, pp. 263-275. https://doi.org/10.1016/j.cvdhj.2022.09.001

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title = "Emerging role of artificial intelligence in cardiac electrophysiology",

abstract = "Artificial intelligence (AI) and machine learning (ML) have significantly impacted the field of cardiovascular medicine, especially cardiac electrophysiology (EP), on multiple fronts. The goal of this review is to familiarize readers with the field of AI and ML and their emerging role in EP. The current review is divided into 3 sections. In the first section, we discuss the definitions and basics of AI, ML, and big data. In the second section, we discuss their application to EP in the context of detection, prediction, and management of arrhythmias. Finally, we discuss the regulatory issues, challenges, and future directions of AI in EP.",

keywords = "Artificial intelligence, Big data, Cardiac electrophysiology, Computational modeling, Deep learning, Machine learning",

author = "Rajesh Kabra and Sharat Israni and Bharat Vijay and Chaitanya Baru and Raghuveer Mendu and Mark Fellman and Arun Sridhar and Pamela Mason and Cheung, \{Jim W.\} and Luigi DiBiase and Srijoy Mahapatra and Jerome Kalifa and Lubitz, \{Steven A.\} and Noseworthy, \{Peter A.\} and Rachita Navara and McManus, \{David D.\} and Mitchell Cohen and Chung, \{Mina K.\} and Natalia Trayanova and Rakesh Gopinathannair and Dhanunjaya Lakkireddy",

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year = "2022",

month = dec,

doi = "10.1016/j.cvdhj.2022.09.001",

language = "English (US)",

volume = "3",

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AU - Kabra, Rajesh

AU - Israni, Sharat

AU - Vijay, Bharat

AU - Baru, Chaitanya

AU - Mendu, Raghuveer

AU - Fellman, Mark

AU - Sridhar, Arun

AU - Mason, Pamela

AU - Cheung, Jim W.

AU - DiBiase, Luigi

AU - Mahapatra, Srijoy

AU - Kalifa, Jerome

AU - Lubitz, Steven A.

AU - Noseworthy, Peter A.

AU - Navara, Rachita

AU - McManus, David D.

AU - Cohen, Mitchell

AU - Chung, Mina K.

AU - Trayanova, Natalia

AU - Gopinathannair, Rakesh

AU - Lakkireddy, Dhanunjaya

PY - 2022/12

Y1 - 2022/12

N2 - Artificial intelligence (AI) and machine learning (ML) have significantly impacted the field of cardiovascular medicine, especially cardiac electrophysiology (EP), on multiple fronts. The goal of this review is to familiarize readers with the field of AI and ML and their emerging role in EP. The current review is divided into 3 sections. In the first section, we discuss the definitions and basics of AI, ML, and big data. In the second section, we discuss their application to EP in the context of detection, prediction, and management of arrhythmias. Finally, we discuss the regulatory issues, challenges, and future directions of AI in EP.

AB - Artificial intelligence (AI) and machine learning (ML) have significantly impacted the field of cardiovascular medicine, especially cardiac electrophysiology (EP), on multiple fronts. The goal of this review is to familiarize readers with the field of AI and ML and their emerging role in EP. The current review is divided into 3 sections. In the first section, we discuss the definitions and basics of AI, ML, and big data. In the second section, we discuss their application to EP in the context of detection, prediction, and management of arrhythmias. Finally, we discuss the regulatory issues, challenges, and future directions of AI in EP.

KW - Artificial intelligence

KW - Big data

KW - Cardiac electrophysiology

KW - Computational modeling

KW - Deep learning

KW - Machine learning

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DO - 10.1016/j.cvdhj.2022.09.001

M3 - Review article

C2 - 36589314

AN - SCOPUS:85141283653

SN - 2666-6936

VL - 3

SP - 263

EP - 275

JO - Cardiovascular Digital Health Journal

JF - Cardiovascular Digital Health Journal

IS - 6

ER -

Emerging role of artificial intelligence in cardiac electrophysiology

Abstract

Keywords

ASJC Scopus subject areas

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