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Evaluating upper airway in orthodontics via 3D UX-Net model on CBCT scans
Yisi Liu1, Jiaqi Zhan2,3,4, Jiaqi Zhang3,5
1Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, Hubei, 430074, China.
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.
