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

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.