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Introduction and aims <p>The relationship between orthodontic treatment and upper airway morphology is increasingly recognized. Artificial intelligence (AI) now supports airway analysis, but traditional 3D U-Net models show limited accuracy, particularly in the laryngopharynx. This study proposes a deep learning model to accurately and efficiently extract 3D upper airway structures from CBCT scans, facilitating improved orthodontic monitoring.</p> Methods <p>The 3D UX-Net was employed for airway segmentation. Biased pharyngeal interface information from the network output enabled precise localization of boundary landmarks on the midsagittal plane, enhancing interface delineation.</p> Results <p>On internal 5-fold cross-validation, 3D UX-Net achieved a mean Dice similarity coefficient (DSC) of 0.953 ± 0.007 for total airway segmentation, outperforming existing methods. External validation across three geographic datasets confirmed strong generalization. After refining the pharyngeal interface via midsagittal landmarks, mean DSC improved to 0.963 ± 0.006.</p> Conclusion <p>The proposed model enables high-precision upper airway segmentation, supporting more efficient and comprehensive clinical image analysis.</p> Clinical relevance <p>This study addresses the insufficient segmentation accuracy of prior 3D U-Net models, especially in the laryngeal region, offering enhanced reliability for orthodontic airway assessment.</p>

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Evaluating upper airway in orthodontics via 3D UX-Net model on CBCT scans

  • 期刊论文

  • 发表日期：

    2026年5月13日

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  • Yisi Liu,
  • Jiaqi Zhan,
  • Jiaqi Zhang,
  • Wentao Hong,
  • Wenjie Fan,
  • Ramy Shihabi,
  • Zhicong Lan,
  • Shuhan Gu,
  • Linqiang Pan,
  • Li Hu

摘要

Introduction and aims

The relationship between orthodontic treatment and upper airway morphology is increasingly recognized. Artificial intelligence (AI) now supports airway analysis, but traditional 3D U-Net models show limited accuracy, particularly in the laryngopharynx. This study proposes a deep learning model to accurately and efficiently extract 3D upper airway structures from CBCT scans, facilitating improved orthodontic monitoring.

Methods

The 3D UX-Net was employed for airway segmentation. Biased pharyngeal interface information from the network output enabled precise localization of boundary landmarks on the midsagittal plane, enhancing interface delineation.

Results

On internal 5-fold cross-validation, 3D UX-Net achieved a mean Dice similarity coefficient (DSC) of 0.953 ± 0.007 for total airway segmentation, outperforming existing methods. External validation across three geographic datasets confirmed strong generalization. After refining the pharyngeal interface via midsagittal landmarks, mean DSC improved to 0.963 ± 0.006.

Conclusion

The proposed model enables high-precision upper airway segmentation, supporting more efficient and comprehensive clinical image analysis.

Clinical relevance

This study addresses the insufficient segmentation accuracy of prior 3D U-Net models, especially in the laryngeal region, offering enhanced reliability for orthodontic airway assessment.