TY - JOUR
T1 - Big Data and Artificial Intelligence
T2 - Opportunities and Threats in Electrophysiology
AU - van de Leur, Rutger R
AU - Boonstra, Machteld J
AU - Bagheri, Ayoub
AU - Roudijk, Rob W
AU - Sammani, Arjan
AU - Taha, Karim
AU - Doevendans, Pieter Afm
AU - van der Harst, Pim
AU - van Dam, Peter M
AU - Hassink, Rutger J
AU - van Es, René
AU - university, Folkert W
N1 - Funding Information:
Disclosure: The authors have no conflicts of interest to declare. Funding: This study was partly supported by The Netherlands Organisation for Health Research and Development (ZonMw, grant number 104021004) and partly supported by the Netherlands Cardiovascular Research Initiative, an initiative with support of the Dutch Heart Foundation (grant numbers CVON2015-12 eDETECT and QRS-VISION 2018B007). FWA is supported by UCL Hospitals NIHR Biomedical Research Center. AS is supported by the UMC Utrecht Alexandre Suerman MD/PhD programme. Received: 8 June 2020 Accepted: 3 August 2020 Citation: Arrhythmia & Electrophysiology Review 2020;9(3):146–54. DOI: https://doi.org/10.15420/aer.2020.26 Correspondence: FW Asselbergs, Department of Cardiology, Division of Heart and Lungs, University Medical Center Utrecht, 3508 GA Utrecht, the Netherlands. E: [email protected]
Publisher Copyright:
© 2020 Radcliffe Group Ltd. All rights reserved.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020/11
Y1 - 2020/11
N2 - 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.
AB - 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.
KW - Artificial intelligence
KW - Big data
KW - Cardiology
KW - Deep learning
KW - ECG
KW - Electrophysiology
KW - Neural networks
UR - http://www.scopus.com/inward/record.url?scp=85098590622&partnerID=8YFLogxK
U2 - 10.15420/aer.2020.26
DO - 10.15420/aer.2020.26
M3 - Review article
C2 - 33240510
SN - 2050-3369
VL - 9
SP - 146
EP - 154
JO - Arrhythmia & electrophysiology review
JF - Arrhythmia & electrophysiology review
IS - 3
ER -