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Big Data and Artificial Intelligence: Opportunities and Threats in Electrophysiology.
van de Leur, Rutger R; Boonstra, Machteld J; Bagheri, Ayoub; Roudijk, Rob W; Sammani, Arjan; Taha, Karim; Doevendans, Pieter Afm; van der Harst, Pim; van Dam, Peter M; Hassink, Rutger J; van Es, René; Asselbergs, Folkert W.
Afiliação
  • van de Leur RR; Department of Cardiology, Division of Heart and Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands.
  • Boonstra MJ; Department of Cardiology, Division of Heart and Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands.
  • Bagheri A; Department of Cardiology, Division of Heart and Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands.
  • Roudijk RW; Department of Methodology and Statistics, Utrecht University, Utrecht, the Netherlands.
  • Sammani A; Department of Cardiology, Division of Heart and Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands.
  • Taha K; Netherlands Heart Institute, Utrecht, the Netherlands.
  • Doevendans PA; Department of Cardiology, Division of Heart and Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands.
  • van der Harst P; Department of Cardiology, Division of Heart and Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands.
  • van Dam PM; Netherlands Heart Institute, Utrecht, the Netherlands.
  • Hassink RJ; Department of Cardiology, Division of Heart and Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands.
  • van Es R; Netherlands Heart Institute, Utrecht, the Netherlands.
  • Asselbergs FW; Central Military Hospital Utrecht, Ministerie van Defensie, Utrecht, the Netherlands.
Arrhythm Electrophysiol Rev ; 9(3): 146-154, 2020 Nov.
Article em En | MEDLINE | ID: mdl-33240510
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.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Arrhythm Electrophysiol Rev Ano de publicação: 2020 Tipo de documento: Article País de afiliação: Holanda

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Arrhythm Electrophysiol Rev Ano de publicação: 2020 Tipo de documento: Article País de afiliação: Holanda