Big Data and Artificial Intelligence: Opportunities and Threats in Electrophysiology

Rutger R van de Leur; Machteld J Boonstra; Ayoub Bagheri; Rob W Roudijk; Arjan Sammani; Karim Taha; Pieter Afm Doevendans; Pim van der Harst; Peter M van Dam; Rutger J Hassink; René van Es; Folkert W Asselbergs

doi:10.15420/aer.2020.26

Big Data and Artificial Intelligence: Opportunities and Threats in Electrophysiology

Arrhythm Electrophysiol Rev. 2020 Nov;9(3):146-154. doi: 10.15420/aer.2020.26.

Authors

Rutger R van de Leur¹, Machteld J Boonstra¹, Ayoub Bagheri^{1

2}, Rob W Roudijk^{1

3}, Arjan Sammani¹, Karim Taha^{1

3}, Pieter Afm Doevendans^{1

3

4}, Pim van der Harst¹, Peter M van Dam¹, Rutger J Hassink¹, René van Es¹, Folkert W Asselbergs^{1

5

6}

Affiliations

¹ Department of Cardiology, Division of Heart and Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands.
² Department of Methodology and Statistics, Utrecht University, Utrecht, the Netherlands.
³ Netherlands Heart Institute, Utrecht, the Netherlands.
⁴ Central Military Hospital Utrecht, Ministerie van Defensie, Utrecht, the Netherlands.
⁵ Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, London, UK.
⁶ Health Data Research UK and Institute of Health Informatics, University College London, London, UK.

Abstract

The combination of big data and artificial intelligence (AI) is having an increasing impact on the field of electrophysiology. Algorithms are created to improve the automated diagnosis of clinical ECGs or ambulatory rhythm devices. Furthermore, the use of AI during invasive electrophysiological studies or combining several diagnostic modalities into AI algorithms to aid diagnostics are being investigated. However, the clinical performance and applicability of created algorithms are yet unknown. In this narrative review, opportunities and threats of AI in the field of electrophysiology are described, mainly focusing on ECGs. Current opportunities are discussed with their potential clinical benefits as well as the challenges. Challenges in data acquisition, model performance, (external) validity, clinical implementation, algorithm interpretation as well as the ethical aspects of AI research are discussed. This article aims to guide clinicians in the evaluation of new AI applications for electrophysiology before their clinical implementation.

Keywords: Artificial intelligence; ECG; big data; cardiology; deep learning; electrophysiology; neural networks.

Publication types

Review