Deep learning algorithms for intelligent construction of a three-dimensional maxillo-facial symmetry reference plane
10.19723/j.issn.1671-167X.2025.01.017
- VernacularTitle:三维颌面对称参考平面智能构建的深度学习算法
- Author:
Yujia ZHU
1
;
Hua SHEN
;
Aonan WEN
;
Zixiang GAO
;
Qingzhao QIN
;
Shenyao SHAN
;
Wenbo LI
;
Xiangling FU
;
Yijiao ZHAO
;
Yong WANG
Author Information
1. 北京大学口腔医学院·口腔医院口腔医学数字化研究中心,国家口腔医学中心,国家口腔疾病临床医学研究中心,口腔生物材料和数字诊疗装备国家工程研究中心,口腔数字医学北京市重点实验室,国家卫生健康委员会口腔医学计算机应用工程技术研究中心,北京 100081
- Publication Type:Journal Article
- Keywords:
Maxillofacial;
Symmetry reference plane;
Imaging,three-dimensional;
Deep learning;
Algorithm
- From:
Journal of Peking University(Health Sciences)
2025;57(1):113-120
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To develop an original-mirror alignment associated deep learning algorithm for intelligent registration of three-dimensional maxillofacial point cloud data,by utilizing a dynamic graph-based registration network model(maxillofacial dynamic graph registration network,MDGR-Net),and to provide a valuable reference for digital design and analysis in clinical dental applications.Methods:Four hundred clinical patients without significant deformities were recruited from Peking University School of Stomatology from October 2018 to October 2022.Through data augmentation,a total of 2 000 three-dimensional maxillofacial datasets were generated for training and testing the MDGR-Net algorithm.These were divided into a training set(1 400 cases),a validation set(200 cases),and an internal test set(200 cases).The MDGR-Net model constructed feature vectors for key points in both original and mirror point clouds(X,Y),established correspondences between key points in the X and Y point clouds based on these feature vectors,and calculated rotation and translation matrices using singular value decomposi-tion(SVD).Utilizing the MDGR-Net model,intelligent registration of the original and mirror point clouds were achieved,resulting in a combined point cloud.The principal component analysis(PCA)algorithm was applied to this combined point cloud to obtain the symmetry reference plane associated with the MDGR-Net methodology.Model evaluation for the translation and rotation matrices on the test set was performed using the coefficient of determination(R2).Angle error evaluations for the three-dimensional maxillofacial symmetry reference planes were constructed using the MDGR-Net-associated method and the"ground truth"iterative closest point(ICP)-associated method were conducted on 200 cases in the inter-nal test set and 40 cases in an external test set.Results:Based on testing with the three-dimensional maxillofacial data from the 200-case internal test set,the MDGR-Net model achieved an R2 value of 0.91 for the rotation matrix and 0.98 for the translation matrix.The average angle error on the internal and external test sets were 0.84°±0.55° and 0.58°±0.43°,respectively.The construction of the three-dimensional maxillofacial symmetry reference plane for 40 clinical cases took only 3 seconds,with the model performing optimally in the patients with skeletal Class Ⅲ malocclusion,high angle cases,and Angle Class Ⅲ orthodontic patients.Conclusion:This study proposed the MDGR-Net association method based on intelligent point cloud registration as a novel solution for constructing three-dimensional maxillo-facial symmetry reference planes in clinical dental applications,which can significantly enhance diagnos-tic and therapeutic efficiency and outcomes,while reduce expert dependence.
