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Status and Opportunities of Machine Learning Applications in Obstructive Sleep Apnea: A Narrative Review.

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Summary

Machine learning shows promise for obstructive sleep apnea (OSA) research but requires more diverse data and robust validation. Future studies must address demographic gaps for equitable AI applications in OSA diagnosis and treatment.

Keywords:
Machine LearningSleep Apnea

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Area of Science:

  • Sleep Medicine
  • Artificial Intelligence in Healthcare
  • Biomedical Data Science

Background:

  • Obstructive sleep apnea (OSA) is a common and serious sleep disorder marked by breathing pauses during sleep.
  • Machine learning (ML) is increasingly used in OSA research for diagnosis, treatment, and understanding disease mechanisms.

Purpose of the Study:

  • To review the application of ML in OSA research.
  • To analyze model performance, datasets, demographics, and validation methods.
  • To identify trends and gaps for future research and clinical decision-making.

Main Methods:

  • A narrative review of 254 PubMed publications (Jan 2018–Mar 2023).
  • Categorization of studies by ML application, models, tasks, validation metrics, data sources, and demographics.

Main Results:

  • ML applications primarily focused on OSA classification and diagnosis using diverse data sources (PSG, ECG, wearables).
  • Study cohorts were mainly overweight males, underrepresenting women, younger obese adults, older adults, and diverse racial groups.
  • Many studies featured small sample sizes and lacked robust model validation.

Conclusions:

  • Inclusive research with larger, representative datasets and bias mitigation is crucial for generalizable ML models in OSA.
  • Addressing demographic and methodological gaps is essential for robust and equitable AI in OSA healthcare.