Abstract：
Sleep-disordered breathing, ranging from habitual snoring to severe obstructive sleep apnea, is a prevalent public health issue. Despite rising interest in sleep and awareness of sleep disorders, sleep research and diagnostic practices still rely on outdated metrics and laborious methods reducing the diagnostic capacity and preventing timely diagnosis and treatment. Consequently, a significant portion of individuals affected by sleep-disordered breathing remain undiagnosed or are misdiagnosed. Taking advantage of state-of-the-art scientific, technological, and computational advances could be an effective way to optimize the diagnostic and treatment pathways. We discuss state-of-the-art multidisciplinary research, review the shortcomings in the current practices of SDB diagnosis and management in adult populations, and provide possible future directions. We critically review the opportunities for modern data analysis methods and machine learning to combine multimodal information, provide a perspective on the pitfalls of big data analysis, and discuss approaches for developing analysis strategies that overcome current limitations. We argue that large-scale and multidisciplinary collaborative efforts based on clinical, scientific, and technical knowledge and rigorous clinical validation and implementation of the outcomes in practice are needed to move the research of sleep-disordered breathing forward, thus increasing the quality of diagnostics and treatment.
摘要：
睡眠呼吸紊乱,从习惯性打鼾到严重的阻塞性睡眠呼吸暂停，是一个普遍的公共卫生问题。尽管人们对睡眠的兴趣和对睡眠障碍的认识不断提高，睡眠研究和诊断实践仍然依赖于过时的指标和费力的方法，从而降低了诊断能力并阻止了及时的诊断和治疗。因此,很大一部分受睡眠呼吸紊乱影响的个体仍未被诊断或被误诊.利用最先进的科学，技术,和计算的进步可能是优化诊断和治疗途径的有效方法。我们讨论最先进的多学科研究，回顾目前SDB在成人人群中的诊断和管理实践中的不足，并提供可能的未来方向。我们批判性地回顾了现代数据分析方法和机器学习结合多模态信息的机会，提供大数据分析陷阱的视角，并讨论开发克服当前局限性的分析策略的方法。我们认为，基于临床的大规模和多学科合作努力，科学,和技术知识和严格的临床验证和实施的结果在实践中需要推进睡眠呼吸紊乱的研究，从而提高诊断和治疗的质量。
