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Drug-Induced Sleep Endoscopy DISE with Target Controlled Infusion TCI and Bispectral Analysis in Obstructive Sleep Apnea
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AI-Assisted Awake Endoscopic Video Analysis for Obstructive Sleep Apnea Detection.

Wen-Sen Lai1,2, Ting-Wei Li3, Chung-Feng Jeffrey Kuo3

  • 1Department of Otolaryngology-Head and Neck Surgery, Taichung Armed Forces General Hospital, Taichung, Taiwan, Republic of China.

Nature and Science of Sleep
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PubMed
Summary
This summary is machine-generated.

This study introduces an AI platform that analyzes awake patient videos to detect obstructive sleep apnea (OSA) quickly and accurately. The system offers a non-invasive screening tool, potentially improving early diagnosis of this common sleep disorder.

Keywords:
AIMuller maneuverdrug-induced sleep endoscopyendoscopic video analysisobstructive sleep apneasemantic segmentation

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Area of Science:

  • Artificial Intelligence in Medicine
  • Medical Imaging Analysis
  • Sleep Medicine Diagnostics

Background:

  • Obstructive sleep apnea (OSA) is a prevalent sleep disorder linked to severe health issues.
  • Current diagnostic methods like polysomnography and DISE are often invasive, costly, or time-consuming.
  • There is a need for rapid, non-invasive, and automated diagnostic tools for OSA.

Purpose of the Study:

  • To develop a fully automated, AI-assisted platform for detecting obstructive sleep apnea (OSA).
  • To utilize nasopharyngoscopic videos acquired during wakefulness for OSA detection.
  • To establish diagnostic criteria comparable to the apnea-hypopnea index (AHI).

Main Methods:

  • An AI system analyzed flexible nasopharyngoscopic videos of awake, supine patients.
  • Image classifiers identified anatomical regions, and segmentation models extracted airway features.
  • Support vector regression predicted OSA using variables like airway cross-sectional area and wall/tongue base ratios.

Main Results:

  • The AI system achieved high classification accuracy for various anatomical regions (up to 98.5%).
  • The prediction model demonstrated 97.14% accuracy on the test set.
  • Diagnostic thresholds for OSA were identified, comparable to AHI, with the entire workflow averaging 85 seconds.

Conclusions:

  • This study presents the first fully automated AI-based dynamic endoscopic video analysis for OSA detection in awake patients.
  • The system non-invasively predicts OSA and identifies potential obstruction sites in real-time.
  • This offers a practical outpatient screening tool to identify candidates for further evaluation.