Evidence map›Paper›PMID 41307859›Full record

ArticleProgress in orthodontics2025

Accuracy comparative study of automatic landmarking and diagnostic models on lateral cephalograms.

Wen-Qing Bu, Zhan-Yi Shi, Zhi-Qiang Tian, Meng-Qi Han, Dong Zhang, Shuo Wang, Yi Sun, Qin Zhou, Zhi-Ping Hu, Fei Wang and 2 more

Abstract readComparative Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Progress in orthodontics, 2025. 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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4 · The record

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5 · Who and what money

Authors and funding

12 authors.

Wen-Qing BuKey Laboratory of Shaanxi Province for Craniofacial Precision Medicine Research, College of Stomatology, Xi'an Jiaotong University, Xi'an, China.
Zhan-Yi ShiState Key Laboratory of Human-Machine Hybrid Augmented Intelligence, National Engineering Research Center for Visual Information and Applications, and Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an, China.
Zhi-Qiang TianState Key Laboratory of Human-Machine Hybrid Augmented Intelligence, National Engineering Research Center for Visual Information and Applications, and Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an, China.
Meng-Qi HanKey Laboratory of Shaanxi Province for Craniofacial Precision Medicine Research, College of Stomatology, Xi'an Jiaotong University, Xi'an, China.
Dong ZhangState Key Laboratory of Human-Machine Hybrid Augmented Intelligence, National Engineering Research Center for Visual Information and Applications, and Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an, China.
Shuo WangState Key Laboratory of Human-Machine Hybrid Augmented Intelligence, National Engineering Research Center for Visual Information and Applications, and Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an, China.
Yi SunDepartment of Imaging & Pathology, Biomedical Sciences Group, KU Leuven & Oral and Maxillofacial Surgery, University Hospitals Leuven, KU Leuven, Leuven, Belgium.
Qin ZhouKey Laboratory of Shaanxi Province for Craniofacial Precision Medicine Research, College of Stomatology, Xi'an Jiaotong University, Xi'an, China.
Zhi-Ping HuKey Laboratory of Shaanxi Province for Craniofacial Precision Medicine Research, College of Stomatology, Xi'an Jiaotong University, Xi'an, China.
Fei WangKey Laboratory of Shaanxi Province for Craniofacial Precision Medicine Research, College of Stomatology, Xi'an Jiaotong University, Xi'an, China.
Shao-Yi DuState Key Laboratory of Human-Machine Hybrid Augmented Intelligence, National Engineering Research Center for Visual Information and Applications, and Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an, China. dushaoyi@xjtu.edu.cn.
Yu-Cheng GuoKey Laboratory of Shaanxi Province for Craniofacial Precision Medicine Research, College of Stomatology, Xi'an Jiaotong University, Xi'an, China. xjtu-guoyucheng@163.com.

Funding

Fundamental Research Funds for the Central Universities xzy022022065Key Research and Development Program of Shaanxi 2021SF-047National Natural Science Foundation of China 81701869Shaanxi Natural Science Funds for Distinguished Young Scholars of China 2025JC-JCQN-101
6 · The paper itself

Abstract

backgroundThe application of deep learning techniques in cephalometric analysis has become increasingly prominent. Although automatic landmarking models for cephalometric analysis have been developed, their accuracy still requires validation and relies heavily on clinicians to resolve discrepancies between results. To address these limitations, automatic diagnostic models have gained attention. However, there is no direct evidence establishing the superiority of one model over the other, especially the generalization and transferability.

methodsBased on the same northern Chinese population external test dataset data and the data of the IEEE (Institute of Electrical and Electronics Engineers) 2015 ISBI (International Symposium on Biomedical Imaging) Grand Challenge dataset, we compared the performance, generalization ability, and transfer ability of the proposed two models, respectively.

resultsOur findings suggest that the automatic landmarking model outperforms the automatic diagnostic model in both external test dataset, with an accuracy of 90.80% on the IEEE dataset.

conclusionsIn this study, the comparison was indirect, with each model having its strengths: the automatic landmarking model offers precise measurements, while the automatic diagnostic model provides quicker results. The choice between them depends on clinical needs, and future work should explore hybrid models to combine the advantages of both.

Indexed as

Anatomic LandmarksCephalometryImage Processing, Computer-AssistedDeep LearningHumansReproducibility of ResultsArtificial intelligenceConvolutional neural networksDecision-makingDeep learningDiagnostic systemsDigital imaging

Identifiers

PMID41307859
PMCPMC12660614

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