人工智能用于睡眠呼吸障碍：方法、数据模态与临床应用分类学

文献信息

中文摘要

背景： 睡眠呼吸障碍（SDB），尤其是阻塞性睡眠呼吸暂停（OSA），是一组高度流行的疾病，与睡眠中断、间歇性低氧血症、日间过度嗜睡、神经认知损害以及心脏代谢疾病相关。尽管实验室多导睡眠监测仍是诊断的核心，但传统睡眠检测可能成本高昂、劳动密集且难以规模化。数据挖掘、机器学习、深度学习、可穿戴传感、移动健康、多模态分析以及大语言模型的最新进展，正在为更可及、个性化和高效的SDB诊疗创造新机会。本综述考察人工智能在SDB诊断和管理中的最新进展，重点关注多导睡眠监测和家庭睡眠呼吸暂停检测、可穿戴和无接触筛查、音频和生理信号分析、影像和解剖风险预测、表型分析和内型分析、治疗监测、持续气道正压通气依从性以及可临床部署的可解释人工智能。我们还讨论大语言模型在患者教育、睡眠报告摘要、临床文档和决策支持工作流程中的新兴作用。最后，我们识别与数据异质性、外部验证、可解释性、隐私、公平性、可重复性、监管以及真实世界整合到睡眠医学中相关的主要挑战。本综述旨在为研究人员和临床医生提供一个循证框架，以理解当前的人工智能应用，并确定SDB精准诊断和管理的未来方向。

英文摘要

Sleep-disordered breathing (SDB), particularly obstructive sleep apnea (OSA), is a highly prevalent group of disorders associated with disrupted sleep, intermittent hypoxemia, excessive daytime sleepiness, neurocognitive impairment, and cardiometabolic morbidity. Although laboratory polysomnography remains central to diagnosis, conventional sleep testing can be costly, labor-intensive, and difficult to scale. Recent advances in data mining, machine learning, deep learning, wearable sensing, mobile health, multimodal analytics, and large language models are creating new opportunities for more accessible, personalized, and efficient SDB care. This review examines recent advances in artificial intelligence for SDB diagnosis and management, with emphasis on polysomnography and home sleep apnea testing, wearable and contactless screening, audio and physiological-signal analysis, imaging and anatomical risk prediction, phenotyping and endotyping, treatment monitoring, continuous positive airway pressure adherence, and clinically deployable explainable AI. We also discuss the emerging role of large language models in patient education, sleep-report summarization, clinical documentation, and decision-support workflows. Finally, we identify major challenges related to data heterogeneity, external validation, interpretability, privacy, fairness, reproducibility, regulation, and real-world integration into sleep medicine. This review aims to provide an evidence-based framework for researchers and clinicians to understand current AI applications and identify future directions for precision diagnosis and management of SDB.