Department of Cardiology, Larnaca General Hospital, Larnaca, Cyprus.
Department of Basic and Clinical Sciences, University of Nicosia Medical School, Nicosia, Cyprus.
J Clin Sleep Med. 2023 Jul 1;19(7):1337-1363. doi: 10.5664/jcsm.10532.
Machine learning (ML) models have been employed in the setting of sleep disorders. This review aims to summarize the existing data about the role of ML techniques in the diagnosis, classification, and treatment of sleep-related breathing disorders.
A systematic search in Medline, EMBASE, and Cochrane databases through January 2022 was performed.
Our search strategy revealed 132 studies that were included in the systematic review. Existing data show that ML models have been successfully used for diagnostic purposes. Specifically, ML models showed good performance in diagnosing sleep apnea using easily obtained features from the electrocardiogram, pulse oximetry, and sound signals. Similarly, ML showed good performance for the classification of sleep apnea into obstructive and central categories, as well as predicting apnea severity. Existing data show promising results for the ML-based guided treatment of sleep apnea. Specifically, the prediction of outcomes following surgical treatment and optimization of continuous positive airway pressure therapy can be guided by ML models.
The adoption and implementation of ML in the field of sleep-related breathing disorders is promising. Advancements in wearable sensor technology and ML models can help clinicians predict, diagnose, and classify sleep apnea more accurately and efficiently.
Bazoukis G, Bollepalli SC, Chung CT, et al. Application of artificial intelligence in the diagnosis of sleep apnea. . 2023;19(7):1337-1363.
机器学习 (ML) 模型已应用于睡眠障碍领域。本综述旨在总结 ML 技术在诊断、分类和治疗与睡眠相关的呼吸障碍中的现有数据。
通过 Medline、EMBASE 和 Cochrane 数据库进行了截至 2022 年 1 月的系统检索。
我们的检索策略显示共有 132 项研究被纳入系统综述。现有数据表明，ML 模型已成功用于诊断目的。具体而言，ML 模型使用心电图、脉搏血氧饱和度和声音信号中易于获得的特征来诊断睡眠呼吸暂停的表现良好。同样，ML 模型在将睡眠呼吸暂停分类为阻塞性和中枢性以及预测呼吸暂停严重程度方面表现良好。基于 ML 的睡眠呼吸暂停治疗的现有数据显示出有希望的结果。具体而言，ML 模型可用于指导预测手术治疗后的结果和优化持续气道正压通气治疗。
在与睡眠相关的呼吸障碍领域采用和实施 ML 具有广阔前景。可穿戴传感器技术和 ML 模型的进步可以帮助临床医生更准确、高效地预测、诊断和分类睡眠呼吸暂停。
Bazoukis G, Bollepalli SC, Chung CT, et al. 人工智能在睡眠呼吸暂停诊断中的应用。睡眠医学评论。2023;19(7):1337-1363.
