人工智能利用光电容积描记术诊断阻塞性睡眠呼吸暂停的准确性：系统评价与荟萃分析

文献信息

中文摘要

背景： 通过多导睡眠监测诊断阻塞性睡眠呼吸暂停（OSA）的传统方法可能昂贵且难以获得。人工智能的最新进展提出使用光电容积描记术（PPG）来辅助OSA诊断。
目的： 本研究旨在评估基于人工智能的方法利用PPG诊断OSA的诊断准确性。
方法： 检索了PubMed、Embase、Scopus、Web of Science和IEEE Xplore，检索时间从建库至2025年11月3日。纳入标准包括观察性研究，这些研究评估了基于人工智能的PPG诊断OSA方法在成人中与传统睡眠检测相比的准确性。我们排除了病例报告、病例系列、综述、荟萃分析、信件、会议摘要、儿科研究、动物研究、外文研究、数据不完整的研究以及关注个体呼吸暂停事件而无患者水平分类的研究。感兴趣的结果是PPG-based AI模型诊断OSA的诊断准确性，如在原始研究中通过随机拆分测试、交叉验证或外部验证所评估。独立评审员提取数据并使用QUADAS-2（诊断准确性研究质量评估-2）工具评估偏倚风险。使用贝叶斯双变量荟萃分析汇总估计值。进一步进行了亚组、敏感性和meta回归分析。使用GRADE（推荐分级的评估、制定与评价）框架评估证据的总体质量。
结果： 从12,579条记录中，我们纳入了13项研究，共包含9,983名参与者。所有研究的偏倚风险被评为低或不明确。总体证据质量为中等。与传统诊断相比，在PPG上训练的人工智能达到了79.6%的汇总敏感性（95%可信区间[CrI] 55.5%-93.8%）和76.5%的特异性（95% CrI 48.2%-94.0%）。随着分类AHI严重程度截断值的增加，总体特异性增加（呼吸暂停低通气指数[AHI] ≥5：63.6%；AHI ≥15：81.8%；AHI ≥30：85.1%），但总体敏感性下降（AHI ≥5：87.2%；AHI ≥15：79.7%；AHI ≥30：76.7%）。此外，深度学习模型比传统机器学习（63.6%）达到了更高的特异性（82.9%）。OSA患病率和设备类型与敏感性或特异性没有明确关联。
结论： 在PPG上训练的人工智能模型具有合理的准确性，可能潜在地作为低成本筛查工具。然而，样本量小、潜在偏倚来源、某些地理区域代表性不足以及潜在混杂因素的影响等局限性凸显了进一步研究的必要性。未来的工作应侧重于深度学习，以提高该方法在初级保健中的可行性和可及性。

英文摘要

BACKGROUND: The traditional method for diagnosing obstructive sleep apnea (OSA) through polysomnography may be expensive and inaccessible. Recent developments in AI propose the use of photoplethysmography (PPG) to aid OSA diagnosis.
OBJECTIVE: This study seeks to evaluate the diagnostic accuracy of an AI-based approach in diagnosing OSA using PPG.
METHODS: PubMed, Embase, Scopus, Web of Science, and IEEE Xplore were searched from inception to November 3, 2025. The inclusion criteria comprised observational studies that evaluated the accuracy of AI-based methods of OSA diagnosis using PPG compared with conventional sleep testing used in adults. We excluded case reports, case series, reviews, meta-analyses, letters, conference abstracts, pediatric studies, animal studies, foreign language studies, studies with incomplete data, and studies focusing on individual apneic events without patient-level classification. The outcome of interest was the diagnostic accuracy of PPG-based AI models for OSA, as assessed in primary studies using random split tests, cross-validation, or external validation. Independent reviewers extracted data and assessed risk of bias using the QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies-2) tool. A Bayesian bivariate meta-analysis was used to pool estimates. Further subgroup, sensitivity, and meta-regression analyses were conducted. Overall quality of evidence was assessed using the GRADE (Grading of Recommendations, Assessment, Development and Evaluations) framework.
RESULTS: From 12,579 records, we included 13 studies, comprising 9983 participants. All studies were rated as either low or unclear for risk of bias. The overall evidence quality was moderate. AI trained on PPG achieved a pooled sensitivity of 79.6% (95% credible interval [CrI] 55.5%-93.8%) and specificity of 76.5% (95% CrI 48.2%-94.0%), compared to conventional diagnosis. The overall specificity increased (apnea-hypopnea index [AHI] ≥5: 63.6%; AHI ≥15: 81.8%; AHI ≥30: 85.1%), but overall sensitivity decreased (AHI ≥5: 87.2%; AHI ≥15: 79.7%; AHI ≥30: 76.7%) with greater categorical AHI severity cutoffs. Additionally, deep learning models achieved a higher specificity (82.9%) than traditional machine learning (63.6%). OSA prevalence and device type were not clearly associated with sensitivity or specificity.
CONCLUSIONS: AI models trained on PPG have reasonable accuracy and may potentially serve as a low-cost screening tool. However, limitations such as small sample sizes, potential sources of bias, underrepresentation of certain geographic regions, and the influence of potential confounding factors highlight the necessity for further research. Future work should focus on deep learning to improve the feasibility and accessibility of this approach in primary care.