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Related Experiment Video

Updated: May 8, 2026

Drug-Induced Sleep Endoscopy (DISE) with Target Controlled Infusion (TCI) and Bispectral Analysis in Obstructive Sleep Apnea
07:54

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Published on: December 6, 2016

Interpretable two-stage deep learning for pediatric obstructive sleep apnea diagnosis using lateral cephalograms.

Jiayi Zhang1,2,3,4, Jiao Tan1,2,3,4, Xuesha Tong1,2,3,4

  • 1The Affiliated Stomatological Hospital of Chongqing Medical University, Chongqing, China.

Frontiers in Pediatrics
|May 7, 2026
PubMed
Summary

An AI framework using lateral cephalograms (LCs) accurately detects pediatric obstructive sleep apnea-hypopnea syndrome (OSAHS). This tool aids early diagnosis and treatment by improving dental professionals' diagnostic accuracy.

Keywords:
artificial intelligencedeep learninglateral cephalogramobstructive sleep apnea hypopnea syndromepediatric

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Healthcare
  • Pediatric Sleep Medicine

Background:

  • Obstructive sleep apnea-hypopnea syndrome (OSAHS) is a common pediatric disorder.
  • Early detection is hindered by limited accessibility and efficiency of current diagnostic methods.

Purpose of the Study:

  • Develop and validate an AI framework for automated, accurate, and interpretable risk evaluation of pediatric OSAHS.
  • Utilize routine lateral cephalograms (LCs) for OSAHS diagnosis.

Main Methods:

  • Retrospective enrollment of 188 children.
  • Development of a two-stage interpretable AI framework using LCs for upper airway segmentation and OSAHS classification.
  • Comparison of different input strategies and use of Grad-CAM for model interpretation.
  • Evaluation of clinical utility via a reader study.

Main Results:

  • High performance in upper airway segmentation (DSC: 0.931, IoU: 0.872).
  • Superior OSAHS classification by the fusion model (AUC: 0.945) compared to LCs-only and ROI-based models.
  • AI assistance significantly improved diagnostic accuracy for dentists of all experience levels.

Conclusions:

  • The AI framework shows promise for automated pediatric OSAHS diagnosis using LCs in dental settings.
  • The model enhances diagnostic accuracy and interpretability, supporting early detection and personalized management.