大数据和人工智能:电生理学的机遇与威胁。

IF 2.6 Q2 CARDIAC & CARDIOVASCULAR SYSTEMS Arrhythmia & Electrophysiology Review Pub Date : 2020-11-01 DOI:10.15420/aer.2020.26
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
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引用次数: 0

摘要

大数据与人工智能(AI)的结合正在对电生理学领域产生越来越大的影响。创建算法是为了改进临床心电图或非卧床心律设备的自动诊断。此外,在侵入性电生理学研究中使用人工智能或将多种诊断模式结合到人工智能算法中以辅助诊断的方法也在研究之中。然而,所创建算法的临床表现和适用性尚不得而知。在这篇叙述性综述中,阐述了人工智能在电生理学领域的机遇和威胁,主要侧重于心电图。讨论了当前的机遇及其潜在的临床益处和挑战。文章讨论了在数据采集、模型性能、(外部)有效性、临床实施、算法解释以及人工智能研究的伦理方面所面临的挑战。这篇文章旨在指导临床医生在临床应用前评估新的人工智能电生理学应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Big Data and Artificial Intelligence: Opportunities and Threats in Electrophysiology.

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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来源期刊
Arrhythmia & Electrophysiology Review
Arrhythmia & Electrophysiology Review CARDIAC & CARDIOVASCULAR SYSTEMS-
CiteScore
5.10
自引率
6.70%
发文量
22
审稿时长
7 weeks
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