State-of-the-art automatic sleep staging methods have already demonstrated comparable reliability and superior time efficiency to manual sleep staging. However, fully automatic black-box solutions are difficult to adapt into clinical workflow and the interaction between explainable automatic methods and the work of sleep technologists remains underexplored and inadequately conceptualized. Thus, we propose a human-in-the-loop concept for sleep analysis, presenting an automatic sleep staging model (aSAGA), that performs effectively with both clinical polysomnographic recordings and home sleep studies. To validate the model, extensive testing was conducted, employing a preclinical validation approach with three retrospective datasets; open-access, clinical, and research-driven. Furthermore, we validate the utilization of uncertainty mapping to identify ambiguous regions, conceptualized as gray areas, in automatic sleep analysis that warrants manual re-evaluation. The results demonstrate that the automatic sleep analysis achieved a comparable level of agreement with manual analysis across different sleep recording types. Moreover, validation of the gray area concept revealed its potential to enhance sleep staging accuracy and identify areas in the recordings where sleep technologists struggle to reach a consensus. In conclusion, this study introduces and validates a concept from explainable artificial intelligence into sleep medicine and provides the basis for integrating human-in-the-loop automatic sleep staging into clinical workflows, aiming to reduce black-box criticism and the burden associated with manual sleep staging.

翻译：现有的自动睡眠分期方法已展现出与人工睡眠分期相当的可信度及更优的时效性。然而，全自动黑箱解决方案难以融入临床工作流程，且可解释性自动睡眠分析技术与睡眠技师工作之间的交互仍未被充分探索和概念化。为此，我们提出一种人机协同的睡眠分析概念，并构建自动睡眠分期模型（aSAGA）。该模型在临床多导睡眠监测记录与家庭睡眠研究数据中均表现出色。为验证模型效能，我们采用临床前验证策略，在三个回顾性数据集（开放获取、临床驱动与研究驱动）上开展广泛测试。此外，我们验证了通过不确定性映射识别模糊区域（即灰色地带）在自动睡眠分析中的价值——这些区域需要人工重新研判。结果表明，自动睡眠分析与各类睡眠记录的人工分析具有相当的一致性水平。灰色地带概念的验证进一步显示，其能提升睡眠分期准确率，并定位睡眠技师难以达成共识的记录区域。本研究首次将可解释人工智能概念引入睡眠医学领域并完成验证，为将人机协同自动睡眠分期整合至临床工作流程奠定基础，旨在减少黑箱批评并降低人工睡眠分期的负担。