Evidence map›Paper›PMID 42128967›Full record

ArticleClinical oral investigations2026

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

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

Abstract read
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In one paragraph

Article in Clinical oral investigations, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

10 authors.

Yisi Liu *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.
Jiaqi Zhan *Department of Stomatology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, China.
Jiaqi ZhangSchool of Stomatology, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, China.
Wentao HongKey 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.
Wenjie FanDepartment of Stomatology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, China.
Ramy ShihabiDepartment of Stomatology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, China.
Zhicong LanOrthodontics Department One, Xiamen Key Laboratory of Stomatological Disease Diagnosis and Treatment, Stomatological Hospital of Xiamen Medical College, Xiamen, Fujian, 361023, China.
Shuhan GuSeventh People's Hospital of Shanghai University of Traditional Chinese Medicine, Shanghai, 200137, China.
Linqiang PanKey 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. lqpan@mail.hust.edu.cn.ORCID http://orcid.org/0000-0002-4554-455X
Li HuDepartment of Stomatology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, China. huli_0316@163.com.ORCID http://orcid.org/0000-0001-6217-9710

Funding

China Oral Health Foundation No. A2023-009Hubei Provincial Natural Science Foundation of China 2022CFB236
6 · The paper itself

Abstract

INTRODUCTION AND

aimsThe 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.

methodsThe 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.

resultsOn 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.

conclusionThe 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.

Indexed as

Cone-Beam Computed TomographyDeep LearningImaging, Three-DimensionalOrthodonticsPharynxHumansAirway analysisComputer visionCone-beam computed tomographyDeep learningMalocclusionOrthodontic treatment

Identifiers

PMID42128967

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.