Evidence map›Paper›PMID 39520662›Full record

ArticleOral radiology2025

Automatic segmentation and visualization of cortical and marrow bone in mandibular condyle on CBCT: a preliminary exploration of clinical application.

Qinxin Wu, Bin Feng, Wenxuan Li, Weihua Zhang, Jun Wang, Xiangping Wang, Jinchen Dai, Chengkai Jin, Fuli Wu, Mengfei Yu and 1 more

Abstract read
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In one paragraph

Article in Oral radiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Qinxin Wu *Department of Maxillofacial Surgery and Oral Implantology, Stomatology Hospital, School of Stomatology, Zhejiang Provincial Clinical Research Center for Oral Diseases, Key Laboratory of Oral Biomedical Research of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, 310003, Zhejiang Province, China.
Bin Feng *Department of Oral and Maxillofacial Radiology, Stomatology Hospital, School of Stomatology, Zhejiang Provincial Clinical Research Center for Oral Diseases, Key Laboratory of Oral Biomedical Research of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, 310000, Zhejiang Province, China.
Wenxuan LiSchool of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, 310014, Zhejiang Province, China.
Weihua ZhangDepartment of Oral and Maxillofacial Radiology, Stomatology Hospital, School of Stomatology, Zhejiang Provincial Clinical Research Center for Oral Diseases, Key Laboratory of Oral Biomedical Research of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, 310000, Zhejiang Province, China.
Jun WangDepartment of Oral and Maxillofacial Radiology, Stomatology Hospital, School of Stomatology, Zhejiang Provincial Clinical Research Center for Oral Diseases, Key Laboratory of Oral Biomedical Research of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, 310000, Zhejiang Province, China.
Xiangping WangDepartment of Oral and Maxillofacial Radiology, Stomatology Hospital, School of Stomatology, Zhejiang Provincial Clinical Research Center for Oral Diseases, Key Laboratory of Oral Biomedical Research of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, 310000, Zhejiang Province, China.
Jinchen DaiDepartment of Oral and Maxillofacial Radiology, Stomatology Hospital, School of Stomatology, Zhejiang Provincial Clinical Research Center for Oral Diseases, Key Laboratory of Oral Biomedical Research of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, 310000, Zhejiang Province, China.
Chengkai JinShanghai Pinghe Bilingual School, Shanghai, 201206, China.
Fuli WuSchool of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, 310014, Zhejiang Province, China. fuliwu@zjut.edu.cn.ORCID 0000-0002-1566-9343
Mengfei YuDepartment of Maxillofacial Surgery and Oral Implantology, Stomatology Hospital, School of Stomatology, Zhejiang Provincial Clinical Research Center for Oral Diseases, Key Laboratory of Oral Biomedical Research of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, 310003, Zhejiang Province, China. yumengfei@zju.edu.cn.ORCID 0000-0002-7700-4697
Fudong ZhuDepartment of Maxillofacial Surgery and Oral Implantology, Stomatology Hospital, School of Stomatology, Zhejiang Provincial Clinical Research Center for Oral Diseases, Key Laboratory of Oral Biomedical Research of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, 310003, Zhejiang Province, China. zfd@zju.edu.cn.

Funding

Medical Health Science and Technology Project of Zhejiang Provincial Health Commission 2023KY372National Natural Science Foundation of China 82471034
6 · The paper itself

Abstract

objectivesTo develop a deep learning-based automatic segmentation method for cortex and marrow in mandibular condyle on cone-beam computed tomography (CBCT) images and explore its clinical application.

methods825 condyles of 490 CBCT images from 3 centers of Stomatology hospital affliated to Zhejiang University School of Medicine were collected. A deep learning model was developed for simultaneous segmentation of cortex and marrow in mandibular condyle. It included a region of interest extraction network and a segmentation network based on 3D U-net, with modifications made to improve the segmentation boundaries. To evaluate its clinical potential, the model's segmentation efficiency and accuracy were compared with those of both junior and senior oral and maxillofacial radiologists. Additionally, the model's ability to assist junior radiologists in diagnosis through visualization and quantitative analysis of the generated 3D model was also assessed.

resultsThe Dice similarity coefficient of the deep learning model was 0.901 (cortex), 0.969 (marrow), and 0.982 (entire condyle). Hausdorff distance was 0.755 mm (cortex), 0.826 mm (marrow), and 0.760 mm (entire condyle). The model outperformed radiologists across all segmentation metrics, completing the task in merely 15.06 s. With the assistance of visualization and quantitative analysis generated from the model's segmentation, the diagnostic accuracy of junior radiologists significantly improved.

conclusionsThe proposed deep learning-based model achieved accurate and efficient segmentation for mandibular condylar cortex and marrow. It possessed capability to generate precise 3D models, facilitating visual quantitative measurement and aiding in the diagnosis of condylar bony changes. This model holds potential for clinical applications in orthognathic surgery, orthodontic treatment, and other TMJ-related interventions.

Indexed as

Bone MarrowCone-Beam Computed TomographyCortical BoneDeep LearningMandibular CondyleAdultFemaleHumansImaging, Three-DimensionalMaleMiddle AgedArtificial intelligenceCone-bean computed tomographyDeep learningMandibular condyle

Identifiers

PMID39520662

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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.