Review and perspective on sleep-disordered breathing research and translation to clinics

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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rising interest in sleep and awareness of sleep disorders, sleep research and diagnostic practices still rely on outdated metrics and laborious methods reducing the diagnostic capacity and preventing timely diagnosis and treatment.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.

Original languageEnglish
Article number101874
Pages (from-to)101874
JournalSleep Medicine Reviews
Volume73
DOIs
Publication statusPublished - Feb 2024

Bibliographical note

Copyright © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved.

Other keywords

  • Big data
  • Machine learning
  • Multidisciplinary research
  • Obstructive sleep apnea
  • Sleep research
  • Sleep-disordered breathing

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