Evaluating upper airway in orthodontics via 3D UX-Net model on CBCT scans.
Authors
Affiliations (11)
Affiliations (11)
- Key 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.
- Department of Stomatology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, China.
- School of Stomatology, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, China.
- Hubei Province Key Laboratory of Oral and Maxillofacial Development and Regeneration, Wuhan, Hubei, 430030, China.
- The First People's Hospital of Lianyungang, Lianyungang, Jiangsu, 222061, China.
- Orthodontics Department One, Xiamen Key Laboratory of Stomatological Disease Diagnosis and Treatment, Stomatological Hospital of Xiamen Medical College, Xiamen, Fujian, 361023, China.
- Seventh People's Hospital of Shanghai University of Traditional Chinese Medicine, Shanghai, 200137, China.
- Key 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. [email protected].
- Department of Stomatology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, China. [email protected].
- School of Stomatology, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, China. [email protected].
- Hubei Province Key Laboratory of Oral and Maxillofacial Development and Regeneration, Wuhan, Hubei, 430030, China. [email protected].
Abstract
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. 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. 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. The proposed model enables high-precision upper airway segmentation, supporting more efficient and comprehensive clinical image analysis. 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.