Boulif Abir, Ananou Bouchra, Ouladsine Mustapha, Delliaux Stéphane
Aix-Marseille University, CNRS, LIS, Marseille, France.
Aix-Marseille University, INSERM, INRAE, C2VN, Marseille, France.
Bioinform Biol Insights. 2023 Feb 10;17:11779322221149600. doi: 10.1177/11779322221149600. eCollection 2023.
In the health care and medical domain, it has been proven challenging to diagnose correctly many diseases with complicated and interferential symptoms, including arrhythmia. However, with the evolution of artificial intelligence (AI) techniques, the diagnosis and prognosis of arrhythmia became easier for the physicians and practitioners using only an electrocardiogram (ECG) examination. This review presents a synthesis of the studies conducted in the last 12 years to predict arrhythmia's occurrence by classifying automatically different heartbeat rhythms. From a variety of research academic databases, 40 studies were selected to analyze, among which 29 of them applied deep learning methods (72.5%), 9 of them addressed the problem with machine learning methods (22.5%), and 2 of them combined both deep learning and machine learning to predict arrhythmia (5%). Indeed, the use of AI for arrhythmia diagnosis is emerging in literature, although there are some challenging issues, such as the explicability of the Deep Learning methods and the computational resources needed to achieve high performance. However, with the continuous development of cloud platforms and quantum calculation for AI, we can achieve a breakthrough in arrhythmia diagnosis.
在医疗保健和医学领域，已证明要正确诊断许多具有复杂和干扰症状的疾病具有挑战性，包括心律失常。然而，随着人工智能（AI）技术的发展，对于仅使用心电图（ECG）检查的医生和从业者来说，心律失常的诊断和预后变得更加容易。本综述对过去12年中通过自动分类不同心跳节律来预测心律失常发生的研究进行了综合。从各种研究学术数据库中，选择了40项研究进行分析，其中29项应用了深度学习方法（72.5%），9项用机器学习方法解决该问题（22.5%），2项将深度学习和机器学习相结合来预测心律失常（5%）。事实上，尽管存在一些具有挑战性的问题，如深度学习方法的可解释性以及实现高性能所需的计算资源，但人工智能在心律失常诊断中的应用正在文献中兴起。然而，随着用于人工智能的云平台和量子计算的不断发展，我们可以在心律失常诊断方面取得突破。
