背景:睡眠呼吸暂停的早期检测,在睡眠期间气流停止或减少的健康状况,是启动及时干预和避免并发症的关键。可穿戴人工智能(AI)将人工智能算法集成到可穿戴设备中,以收集和分析数据,提供各种功能和见解,由于其便利性,可以有效地检测睡眠呼吸暂停,可访问性,负担能力,客观性,和实时监控功能,从而解决了多导睡眠图等传统方法的局限性。
目的:本系统评价的目的是检查可穿戴AI在检测睡眠呼吸暂停方面的有效性,它的类型,及其严重程度。
方法:我们在6个电子数据库中进行搜索。这篇综述包括英文研究文章,评估可穿戴AI在识别睡眠呼吸暂停方面的表现,区分其类型,并衡量其严重性。两名研究人员独立进行研究选择,提取的数据,并使用经修改的诊断准确性研究质量评估工具评估偏倚风险。我们使用了叙事和统计技术进行证据综合。
结果:在615项研究中,38人(6.2%)符合本次审查的资格标准。合并平均准确度,灵敏度,可穿戴AI检测呼吸暂停事件(呼吸暂停和非呼吸暂停事件)的特异性分别为0.893,0.793和0.947.可穿戴AI在区分呼吸暂停事件类型方面的合并平均准确性(正常,阻塞性睡眠呼吸暂停,中枢性睡眠呼吸暂停,混合性呼吸暂停,和低通气)为0.815。合并平均准确度,灵敏度,可穿戴AI检测睡眠呼吸暂停的特异性分别为0.869、0.938和0.752。可穿戴AI在识别睡眠呼吸暂停的严重程度方面的汇总平均准确性(正常,温和,中度,和严重)和估计严重程度评分(呼吸暂停低通气指数)分别为0.651和0.877。亚组分析发现,不同结果的可穿戴AI性能的不同主持人,例如算法的类型,数据类型,睡眠呼吸暂停的类型,和可穿戴设备的放置。
结论:可穿戴AI在识别和分类睡眠呼吸暂停方面显示出潜力,但其目前的性能对于常规临床应用来说并不理想。我们建议与传统评估同时使用,直到改进的证据支持其可靠性。需要经过认证的商用可穿戴设备来有效检测睡眠呼吸暂停,预测它的发生,并提供积极的干预措施。研究人员应该对检测中枢睡眠呼吸暂停进行进一步研究,优先考虑深度学习算法,整合自我报告和不可穿戴的数据,评估不同设备放置的性能,并为有效的荟萃分析提供详细的结果。
BACKGROUND: Early detection of sleep apnea, the health condition where airflow either ceases or decreases episodically during sleep, is crucial to initiate timely interventions and avoid complications. Wearable artificial intelligence (AI), the integration of AI algorithms into wearable devices to collect and analyze data to offer various functionalities and insights, can efficiently detect sleep apnea due to its convenience, accessibility, affordability, objectivity, and real-time monitoring capabilities, thereby addressing the limitations of traditional approaches such as polysomnography.
OBJECTIVE: The objective of this systematic review was to examine the effectiveness of wearable AI in detecting sleep apnea, its type, and its severity.
METHODS: Our search was conducted in 6 electronic databases. This review included English research articles evaluating wearable AI\'s performance in identifying sleep apnea, distinguishing its type, and gauging its severity. Two researchers independently conducted study selection, extracted data, and assessed the risk of bias using an adapted Quality Assessment of Studies of Diagnostic Accuracy-Revised tool. We used both narrative and statistical techniques for evidence synthesis.
RESULTS: Among 615 studies, 38 (6.2%) met the eligibility criteria for this review. The pooled mean accuracy, sensitivity, and specificity of wearable AI in detecting apnea events in respiration (apnea and nonapnea events) were 0.893, 0.793, and 0.947, respectively. The pooled mean accuracy of wearable AI in differentiating types of apnea events in respiration (normal, obstructive sleep apnea, central sleep apnea, mixed apnea, and hypopnea) was 0.815. The pooled mean accuracy, sensitivity, and specificity of wearable AI in detecting sleep apnea were 0.869, 0.938, and 0.752, respectively. The pooled mean accuracy of wearable AI in identifying the severity level of sleep apnea (normal, mild, moderate, and severe) and estimating the severity score (Apnea-Hypopnea Index) was 0.651 and 0.877, respectively. Subgroup analyses found different moderators of wearable AI performance for different outcomes, such as the type of algorithm, type of data, type of sleep apnea, and placement of wearable devices.
CONCLUSIONS: Wearable AI shows potential in identifying and classifying sleep apnea, but its current performance is suboptimal for routine clinical use. We recommend concurrent use with traditional assessments until improved evidence supports its reliability. Certified commercial wearables are needed for effectively detecting sleep apnea, predicting its occurrence, and delivering proactive interventions. Researchers should conduct further studies on detecting central sleep apnea, prioritize deep learning algorithms, incorporate self-reported and nonwearable data, evaluate performance across different device placements, and provide detailed findings for effective meta-analyses.