Evidence map›Paper›PMID 39856517›Full record

ArticleBeijing da xue xue bao. Yi xue ban = Journal of Peking University. Health sciences2025

[Establishment and evaluation of a similarity measurement model for orthognathic patients based on the 3D craniofacial features].

Ling Wu, Jiakun Fang, Xiaojing Liu, Zili Li, Yang Li, Xiaoxia Wang

Abstract readEnglish Abstract
In one paragraph

Article in Beijing da xue xue bao. Yi xue ban = Journal of Peking University. Health sciences, 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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1citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Ling WuDepartment of Oral and Maxillofacial Surgery, Peking University School and Hospital of Stomatology & National Center of Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices, Beijing 100081, China.
Jiakun FangDepartment of Oral and Maxillofacial Surgery, Peking University School and Hospital of Stomatology & National Center of Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices, Beijing 100081, China.
Xiaojing LiuDepartment of Oral and Maxillofacial Surgery, Peking University School and Hospital of Stomatology & National Center of Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices, Beijing 100081, China.
Zili LiDepartment of Oral and Maxillofacial Surgery, Peking University School and Hospital of Stomatology & National Center of Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices, Beijing 100081, China.
Yang LiDepartment of Oral and Maxillofacial Surgery, Peking University School and Hospital of Stomatology & National Center of Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices, Beijing 100081, China.
Xiaoxia WangDepartment of Oral and Maxillofacial Surgery, Peking University School and Hospital of Stomatology & National Center of Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices, Beijing 100081, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo establish a similarity measurement model for patients with dentofacial deformity based on 3D craniofacial features and to validate the similarity results with quantifying subjective expert scoring.

methodsIn the study, 52 cases of patients with skeletal Class Ⅲ malocclusions who underwent bimaxillary surgery and preoperative orthodontic treatment at Peking University School and Hospital of Stomatology from January 2020 to December 2022, including 26 males and 26 females, were selected and divided into 2 groups by sex. One patient in each group was randomly selected as a reference sample, and the others were set as test samples. Three senior surgeons rated the similarity scores between the test samples and the reference sample. Similarity scores ranged from 1 to 10, where 1 was completely different, and 10 was exactly the same. Scores larger than 7.5 was considered as clinically similar. Preoperative cone beam computed tomography (CBCT) and 3D facial images of the patients were collected. The three-dimensional hard and soft tissue features, including distances, angles and 3D point cloud features were extracted. The similarity measurement model was then established to fit with the experts' similarity scoring by feature selection algorithm and linear regression model. To verify the reliability of the model, 14 new patients were selected and input to similarity measurement model for finding similar cases. The similarity scoring of these similar cases were rated by experts, and used to evaluate the reliability of the model.

resultsThe similarity metric models indicated that the features of the middle and lower craniofacial features were the main features to influence the craniofacial similarity. The main features that were related to the expert' s similarity scoring included distance of anterior nasal spine-menton (ANS-Me), distance of right upper canion point-Frankfurt horizontal plane (U3RH), distance of left superior point of the condyle-left gonion (CoL-GoL), distance of left gonion-menton (CoL-Me), distance of pogonion-midsagittal plane (Pog-MSP), distance of right alar base-left alar base (AlR-AlL), angle of pronasale-soft tissue pogonion-labrale inferius (Pn-Pog' -Li), distance of trichion-right tragus (Tri-TraR), distance of left exocanthion-left alar base (ExL-AlL), lower 1/3 of skeletal face, middle and lower 2/3 of skeletal face and upper lip region of soft tissue. Fourteen new patients were chosen to evaluate the model. The similar cases selected by the model had an average experts' similarity scoring of 7.627± 0.711, which was not significantly different with 7.5.

conclusionThe similarity measurement model established by this model could find the similar cases which highly matched experts' subjective similarity scoring. The study could be further used for similar cases retrieval in skeletal Ⅲ malocclusion patients.

Indexed as

Dentofacial DeformitiesFaceImaging, Three-DimensionalMalocclusion, Angle Class IIIOrthognathic Surgical ProceduresAdolescentAdultCephalometryCone-Beam Computed TomographyFemaleHumansMaleYoung Adult3D morphological featuresCraniofacial similarityDentofacial deformities

Identifiers

PMID39856517
PMCPMC11759787

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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.