Evidence map›Paper›PMID 42228150›Full record

ArticleClinical oral investigations2026

Integrating deep learning techniques for analysis of chin morphology among Han Chinese individuals using a large cone-beam computed tomography dataset.

Boxuan Xu, Changcheng Song, Yichen Pan, Wei Li, Kaichen Nie, Yuru Pei, Mingyang Ma, Tong Yang, Hong Su, Gui Chen

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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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10 authors.

Boxuan Xu *Peking University School and Hospital of Stomatology, Beijing, 100191, China.
Changcheng Song *Peking University School and Hospital of Stomatology, Beijing, 100191, China.
Yichen PanDepartment of Oral and Maxillofacial-Head Neck Oncology, College of Stomatology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200011, China.
Wei LiDepartment of Orthodontics, National Engineering Laboratory for Digital and Material Technology of Stomatology, Beijing Key Laboratory of Digital Stomatology, Peking University School and Hospital of Stomatology, Beijing, 100191, China.
Kaichen NieKey Laboratory of Machine Perception (MOE), Department of Machine Intelligence, School of Intelligence Science and Technology, Peking University, Beijing, 100871, China.
Yuru PeiKey Laboratory of Machine Perception (MOE), Department of Machine Intelligence, School of Intelligence Science and Technology, Peking University, Beijing, 100871, China.
Mingyang MaShanghai Linkedsmile Orthodontic Information Technology Co., Ltd, Shanghai, 201203, China.
Tong YangShanghai Linkedsmile Orthodontic Information Technology Co., Ltd, Shanghai, 201203, China.
Hong Su *Department of Orthodontics, National Engineering Laboratory for Digital and Material Technology of Stomatology, Beijing Key Laboratory of Digital Stomatology, Peking University School and Hospital of Stomatology, Beijing, 100191, China. suhong0309@qq.com.
Gui Chen *Department of Orthodontics, National Engineering Laboratory for Digital and Material Technology of Stomatology, Beijing Key Laboratory of Digital Stomatology, Peking University School and Hospital of Stomatology, Beijing, 100191, China. chengui723@163.com.

Funding

Beijing Natural Science Foundation L242134
6 · The paper itself

Abstract

objectivesTo characterize chin morphology and investigate its associations with sex, as well as various sagittal and vertical skeletal patterns. MATERIALS AND

methodsA total of 743 cone-beam computed tomography (CBCT) images (322 males, 421 females; aged 18-83 years) were analyzed to measure chin height, thickness, and width. An nnU-Net model was trained for automated chin segmentation using a subset of 304 images. The dataset was stratified into 18 categories based on combinations of sex and skeletal patterns (sagittal and vertical). Aligned average chin models were then constructed to facilitate morphological comparisons across these groups.

resultsMorphometric analysis revealed that males possessed significantly greater chin height (22.54 ± 3.06 mm vs. 20.87 ± 2.45 mm, p < 0.01) and width (51.25 ± 3.36 mm vs. 50.15 ± 2.87 mm, p < 0.01) than females, whereas no significant difference was observed in thickness. Regarding sagittal patterns, chin thickness emerged as the primary distinguishing feature; Class II individuals exhibited significantly thinner chins (4.74 ± 1.02 mm) compared to both Class I (5.16 ± 1.04 mm) and Class III (5.18 ± 0.98 mm) individuals (p < 0.01), a trend consistent across both sexes. In terms of vertical skeletal patterns, low-angle individuals were characterized by significantly greater chin thickness (5.26 ± 1.10 mm, p < 0.001) and width (51.40 ± 2.99 mm, p < 0.01), but reduced height (21.04 ± 2.74 mm, p < 0.001) relative to high-angle and average-angle groups. Comparative analysis of 3D average chin models corroborated these morphometric findings, providing visual evidence of distinct shape variations.

conclusionsThis study systematically characterized chin morphology variations in relation to sex and skeletal patterns, establishing comprehensive average models specifically for the Han Chinese population. These findings provide a high-fidelity anatomical reference that can enhance diagnostic precision and treatment planning in orthodontics and orthognathic surgery. CLINICAL RELEVANCE: Chin prominence is a decisive factor in orthodontic treatment planning and facial profiling. Furthermore, chin thickness is closely correlated with the alveolar bone volume of the mandibular anterior teeth, acting as a primary anatomical constraint for tooth movement. This morphological insight is particularly critical for planning safe and stable orthodontic outcomes in patients with skeletal Class II or high-angle patterns, where bone support may be limited.

Indexed as

ChinCone-Beam Computed TomographyDeep LearningAdolescentAdultAgedAged, 80 and overChinaEast Asian PeopleFemaleHumansMaleMiddle AgedYoung AdultCBCTChinDeep learningHan ChineseMorphology

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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.