Tran Nguyen Binh Minh Hoang, Tran Thi Quynh Trang, Tsai Cheng-Yu, Kang Jiunn-Horng
International Ph.D. Program in Medicine, College of Medicine, Taipei Medical University, Taipei 11031, Taiwan.
Department of Rehabilitation, Ho Chi Minh City Hospital for Rehabilitation and Professional Diseases, Ho Chi Minh City 700000, Vietnam.
Diagnostics (Basel). 2025 Aug 21;15(16):2111. doi: 10.3390/diagnostics15162111.
: Obstructive sleep apnea (OSA) is one of the most prevalent yet underdiagnosed sleep disorders. We evaluated the diagnostic accuracy of radar-based systems and ranked frequency bands for the non-contact detection of OSA. : A systematic search of six databases was conducted from inception to May 23, 2025. Eligible studies included adults assessed for OSA using radar-based systems compared to polysomnography. Hierarchical SROC modeling, threshold-based meta-analyses, and frequency band-stratified network meta-analysis were performed. Certainty of evidence was assessed using GRADE. The PROSPERO registration number is CRD420251059236. : We identified 23,906 records and included 20 studies involving 1540 participants. The primary outcome included a high area under the curve (AUC) of approximately 0.91, an optimal apnea-hypopnea index (AHI) cutoff of ≥22 with a sensitivity of 0.8155 (95% confidence interval (CI): 0.6862-0.8993) and specificity of 0.8819 (95% CI: 0.7799-0.9402). At an AHI threshold of 30, X-band dual radar performed the best, followed by K-band, which yielded significant but more variable results. C-bands consistently showed lower diagnostic values. : This study provides a novel radar band comparison for OSA detection, highlighting clinically relevant thresholds. Key limitations are indirect comparisons and limited, varied samples. Radar-based systems show high sensitivity for OSA detection, optimized by frequency, radar type, artificial intelligence support, and dual sensors within 0.2-1.5 m. Future work should expand the frequency analysis, standardize AHI thresholds, and validate results in specific subgroups.
阻塞性睡眠呼吸暂停(OSA)是最常见但诊断不足的睡眠障碍之一。我们评估了基于雷达的系统的诊断准确性,并对用于OSA非接触式检测的频段进行了排名。:从数据库创建到2025年5月23日对六个数据库进行了系统检索。符合条件的研究包括使用基于雷达的系统与多导睡眠图相比评估OSA的成年人。进行了分层SROC建模、基于阈值的荟萃分析和频段分层网络荟萃分析。使用GRADE评估证据的确定性。PROSPERO注册号为CRD420251059236。:我们识别出23906条记录,纳入了20项研究,涉及1540名参与者。主要结果包括曲线下面积(AUC)约为0.91,最佳呼吸暂停低通气指数(AHI)截断值≥22,敏感性为0.8155(95%置信区间(CI):0.6862 - 0.8993),特异性为0.8819(95%CI:0.7799 - 0.9402)。在AHI阈值为30时,X波段双雷达表现最佳,其次是K波段,其结果显著但变化更大。C波段始终显示出较低的诊断价值。:本研究为OSA检测提供了一种新的雷达频段比较,突出了临床相关阈值。主要局限性是间接比较和样本有限且多样。基于雷达的系统对OSA检测显示出高敏感性,可通过频率、雷达类型、人工智能支持以及0.2 - 1.5米内的双传感器进行优化。未来的工作应扩大频率分析,标准化AHI阈值,并在特定亚组中验证结果。