Two-Phase Deep Learning Approach for Diagnosing Pediatric Obstructive Sleep Apnea Using Lateral Cephalometric Images
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By
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Jiayi Zhang
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Jiao Tan
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Xuesha Tong
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Huiya Wang
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Yue Zhao
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Jinlin Song
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Yang Liu
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April 21, 2026
Clinical Scorecard: Two-Phase Deep Learning Approach for Diagnosing Pediatric Obstructive Sleep Apnea Using Lateral Cephalometric Images
At a Glance
| Category | Detail |
|---|
| Condition | Pediatric Obstructive Sleep Apnea-Hypopnea Syndrome (OSAHS) |
| Key Mechanisms | Intermittent partial or complete obstruction of the upper airway during sleep |
| Target Population | Children aged 1-18 years |
| Care Setting | Dental and orthodontic practices |
Key Highlights
- Developed an AI framework for automated diagnosis of pediatric OSAHS using lateral cephalograms
- Achieved an AUC of 0.945 for OSAHS classification with a fusion model
- AI assistance improved diagnostic accuracy for dentists by up to 0.237
- Upper airway segmentation demonstrated high accuracy with a mean DSC of 0.931
- Routine LCs provide a low-radiation, accessible diagnostic tool for pediatric OSAHS
Guideline-Based Recommendations
Diagnosis
- Utilize lateral cephalograms for opportunistic screening of pediatric OSAHS
- Consider AI-based models for enhanced diagnostic accuracy
Management
- Implement early screening and timely intervention strategies for affected children
Monitoring & Follow-up
- Regular assessment of apnea-hypopnea index (AHI) to evaluate severity
Risks
- Monitor for comorbidities such as cognitive impairments and behavioral issues
Patient & Prescribing Data
Children diagnosed with OSAHS based on AHI criteria
AI framework can assist in identifying at-risk patients for timely management
Clinical Best Practices
- Incorporate AI tools in routine dental assessments for OSAHS screening
- Use Grad-CAM for visual interpretation of diagnostic models
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