Evidence map›Paper›PMID 40340630›Full record

ArticleBMC oral health2025

Quantitative analysis and clinical determinants of orthodontically induced root resorption using automated tooth segmentation from CBCT imaging.

Jiaqi Lin, Qianhan Zheng, Yongjia Wu, Mengqi Zhou, Jiahao Chen, Xiaozhe Wang, Ting Kang, Weifang Zhang, Xuepeng Chen

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC oral health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed, 3 pooled it
–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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

11 citing papers in PubMed, 3 syntheses or guidelines pooled it.

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4 · The record

Corrections and comments

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

9 authors.

Jiaqi Lin *School of Stomatology, Clinical Research Center for Oral Diseases of Zhejiang Province, Stomatology HospitalZhejiang University School of MedicineKey Laboratory of Oral Biomedical Research of Zhejiang Province, Cancer Center of Zhejiang University, Hangzhou, 310006, China.
Qianhan Zheng *School of Stomatology, Clinical Research Center for Oral Diseases of Zhejiang Province, Stomatology HospitalZhejiang University School of MedicineKey Laboratory of Oral Biomedical Research of Zhejiang Province, Cancer Center of Zhejiang University, Hangzhou, 310006, China.
Yongjia WuSchool of Stomatology, Clinical Research Center for Oral Diseases of Zhejiang Province, Stomatology HospitalZhejiang University School of MedicineKey Laboratory of Oral Biomedical Research of Zhejiang Province, Cancer Center of Zhejiang University, Hangzhou, 310006, China.
Mengqi ZhouSchool of Stomatology, Clinical Research Center for Oral Diseases of Zhejiang Province, Stomatology HospitalZhejiang University School of MedicineKey Laboratory of Oral Biomedical Research of Zhejiang Province, Cancer Center of Zhejiang University, Hangzhou, 310006, China.
Jiahao ChenSchool of Stomatology, Clinical Research Center for Oral Diseases of Zhejiang Province, Stomatology HospitalZhejiang University School of MedicineKey Laboratory of Oral Biomedical Research of Zhejiang Province, Cancer Center of Zhejiang University, Hangzhou, 310006, China.
Xiaozhe WangSchool of Stomatology, Clinical Research Center for Oral Diseases of Zhejiang Province, Stomatology HospitalZhejiang University School of MedicineKey Laboratory of Oral Biomedical Research of Zhejiang Province, Cancer Center of Zhejiang University, Hangzhou, 310006, China.
Ting KangSchool of Stomatology, Clinical Research Center for Oral Diseases of Zhejiang Province, Stomatology HospitalZhejiang University School of MedicineKey Laboratory of Oral Biomedical Research of Zhejiang Province, Cancer Center of Zhejiang University, Hangzhou, 310006, China. drkangting@qq.com.
Weifang ZhangSchool of Stomatology, Clinical Research Center for Oral Diseases of Zhejiang Province, Stomatology HospitalZhejiang University School of MedicineKey Laboratory of Oral Biomedical Research of Zhejiang Province, Cancer Center of Zhejiang University, Hangzhou, 310006, China. chzwf@zju.edu.cn.
Xuepeng ChenSchool of Stomatology, Clinical Research Center for Oral Diseases of Zhejiang Province, Stomatology HospitalZhejiang University School of MedicineKey Laboratory of Oral Biomedical Research of Zhejiang Province, Cancer Center of Zhejiang University, Hangzhou, 310006, China. cxp1979@zju.edu.cn.

Funding

Fundamental Research Funds for the Central Universities 2023QZJH60Funds of the Central Government Guiding Local Science and Technology Development 2023ZY1060Key Research and Development Program of Zhejiang Province 2023C03072R&D Program of the Stomatology Hospital of Zhejiang University School of Medicine RD2022DLYB03
6 · The paper itself

Abstract

backgroundOrthodontically induced root resorption (OIRR) is difficult to assess accurately using traditional 2D imaging due to distortion and low sensitivity. While CBCT offers more precise 3D evaluation, manual segmentation remains labor-intensive and prone to variability. Recent advances in deep learning enable automatic, accurate tooth segmentation from CBCT images. This study applies deep learning and CBCT technology to quantify OIRR and analyze its risk factors, aiming to improve assessment accuracy, efficiency, and clinical decision-making.

methodThis study retrospectively analyzed CBCT scans of 108 orthodontic patients to assess OIRR using deep learning-based tooth segmentation and volumetric analysis. Statistical analysis was performed using linear regression to evaluate the influence of patient-related factors. A significance level of p < 0.05 was considered statistically significant.

resultsRoot volume significantly decreased after orthodontic treatment (p < 0.001). Age, gender, open (deep) bite, severe crowding, and other factors significantly influenced root resorption rates in different tooth positions. Multivariable regression analysis showed these factors can predict root resorption, explaining 3% to 15.4% of the variance.

conclusionThis study applied a deep learning model to accurately assess root volume changes using CBCT, revealing significant root volume reduction after orthodontic treatment. It found that underage patients experienced less root resorption, while factors like anterior open bite and deep overbite influenced resorption in specific teeth, though skeletal pattern, overjet, and underbite were not significant predictors.

Indexed as

Cone-Beam Computed TomographyOrthodontics, CorrectiveRoot ResorptionAdolescentAdultChildDeep LearningFemaleHumansImaging, Three-DimensionalMaleRetrospective StudiesRisk FactorsYoung AdultCone-beam computed tomographyLinear regressionOrthodontically induced root resorptionRisk factor

Identifiers

PMID40340630
PMCPMC12063342

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LicenceCC BY-NC-ND
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Registered trials

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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.