Research on automated orthodontic tooth arrangement by integrating multi-source structural information and dental arch prediction_Journal of Biomedical Engineering

Authors：

FANG Yuhang ¹ , ZHAO Zheng ¹ , ZHENG Hongbo ¹ ,  QIN Xujia ¹ , ZHU Yuanxiang ² , WU Zhengqiang ²

1. College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, P. R. China;
2. Hangzhou CEDU Technology Co., Ltd., Hangzhou 310023, P. R. China;

Corresponding?author：

QIN Xujia, Email: qxj@zjut.edu.cn

Keywords：

Digital orthodontics; Automatic tooth arrangement; Dental arch line fitting; Collision loss function

DOI：

10.7507/1001-5515.202510064

Video：

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Abstract Full text Figures/Tables Video References Cited by

To address the issues of time-consuming manual tooth alignment, reliance on physician experience, and insufficient structural constraints in automated methods in traditional orthodontic treatment planning, this paper proposes an automated tooth alignment network model that integrates multi-source geometric information learning. First, this study uses tooth mesh data as input to construct a graph convolution-based network for extracting tooth geometric features, fully utilizing the topological connectivity and local geometric features of the tooth model to enhance the extraction of tooth morphological features. Then, an arch line prediction network is designed to guide the teeth to align orderly along the natural arch line by explicitly modeling the tooth alignment trajectory, thereby ensuring the anatomical rationality and aesthetics of the overall structure. Based on this, this study designs collision avoidance loss and arch alignment loss to reduce geometric conflicts between adjacent teeth and constrain the overall posture, making the alignment results more stable and realistic. To verify the effectiveness of the method, validation experiments were conducted on multiple sets of real dental arch data, and comparisons were made with mainstream methods such as tooth alignment network (TaligNet), parameterized spatial transformation network (PSTN), and Transformer-based network for tooth alignment (TANet). The results show that the proposed method improves the evaluation metrics to varying degrees, fully integrates global and local geometric information, improves tooth posture coordination and alignment smoothness, and provides efficient and standardized intelligent support for digital orthodontic treatment planning.

Citation： FANG Yuhang, ZHAO Zheng, ZHENG Hongbo, QIN Xujia, ZHU Yuanxiang, WU Zhengqiang. Research on automated orthodontic tooth arrangement by integrating multi-source structural information and dental arch prediction. Journal of Biomedical Engineering, 2026, 43(3): 571-579, 588. doi: 10.7507/1001-5515.202510064 Copy

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2.	陳佳懿, 章燕珍. 正畸治療與顳下頜關節問題的研究進展. 臨床醫學進展, 2025, 15(4): 1473-1478..
3.	胡煒, 傅民魁. 口腔正畸治療要點Ⅳ. 正畸治療中的口腔健康教育和衛生保健. 中華口腔醫學雜志, 2006, 41(5): 313-315..
4.	Litjens G, Kooi T, Bejnordi B E, et al. A survey on deep learning in medical image analysis. Med Image Anal, 2017, 42(1): 60-88..
5.	Huang C X, Wang J J, Wang S H, et al. A review of deep learning in dentistry. Neurocomputing, 2023, 554: 126629..
6.	Yu H, Liu L D, Chen S M, et al. Collision-free path planning method for digital orthodontic treatment. Graph Models, 2025, 141: 101297..
7.	楊航, 陳瑞, 安仕鵬. 深度學習背景下的圖像三維重建技術進展綜述. 中國圖象圖形學報, 2023, 28(8): 2396-2409..
8.	李占利, 付敬鼎, 李洪安, 等. 虛擬正畸治療中的錯位牙齒自動排列方法. 圖學學報, 2019, 40(2): 225-232..
9.	陶樂然, 張容斌, 林郁欣, 等. 深度學習在牙頜面畸形診療中的應用及研究進展. 中國口腔頜面外科雜志, 2022, 20(4): 405-409..
10.	徐琦, 張曉蓉. 牙周正畸治療的研究進展. 臨床口腔醫學雜志, 2014, 28(4): 249-251..
11.	胡夢杰, 方宇航, 秦緒佳, 等. 基于網格特征的自動排牙方法. 中國機械工程, 2025, 36(11): 2738-2746..
12.	李笙銀, 周建萍. 深度學習在正畸診斷和治療中的應用進展. 臨床醫學進展, 2025, 15(9): 1577-1585..
13.	Lee M K, Allareddy V, Rampa S, et al. Applications and challenges of implementing artificial intelligence in orthodontics: a primer for orthodontists. Semin Orthod, 2024, 30(1): 72-76..
14.	Cui Z, Fang Y, Mei L, et al. A fully automatic AI system for tooth and alveolar bone segmentation from cone-beam CT images. Nat Commun, 2022, 13(1): 2096..
15.	Yang L C, Shi Z F, Wu Y Q, et al. iOrthopredictor: model-guided deep prediction of teeth alignment. ACM Trans Graph, 2020, 39(6): 216..
16.	Li X S, Bi L, Kim J M, et al. Malocclusion treatment planning via PointNet-based spatial transformation network//Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), Lima: Springer, 2020: 105-114..
17.	Wei G D, Cui Z M, Liu Y M, et al. TANet: towards fully automatic tooth arrangement//Computer Vision-ECCV 2020: 16th European Conference (ECCV), Glasgow: Springer, 2020: 481-497..
18.	Wang Y, Sun Y, Liu Z, et al. Dynamic graph CNN for learning on point clouds. ACM Trans Graph, 2019, 38(5): 146..
19.	Deng Q, Yang X, Huang M, et al. TAPoseNet: teeth alignment based on pose estimation via multi-scale graph convolutional network//Medical Image Computing and Computer Assisted Intervention (MICCAI), Marrakesh: Springer, 2024: 314-323..
20.	Zheng Y, Chen B, Shen Y, et al. TeethGNN: semantic 3D teeth segmentation with graph neural networks. IEEE Trans Vis Comput Graph, 2023, 29(7): 3158-3168..
21.	Dosovitskiy A, Beyer L, Kolesnikov A, et al. An image is worth 16x16 words: transformers for image recognition at scale. arXiv, 2020: 2010.11929..
22.	Lei C, Xia M, Wang S, et al. Automatic tooth arrangement with joint features of point and mesh representations via diffusion probabilistic models. Comput Aided Geom Des, 2024, 111: 102293..
23.	白煜, 高盟, 劉冬梅, 等. 骨性Ⅱ類錯牙合下頜基骨與牙弓形態的研究. 口腔醫學, 2025, 45(6): 436-439..
24.	束傳亮, 江煜, 蔡佳. 成年女性上頜前牙美學區牙列擁擠度與基骨形態、牙弓和牙槽弓形態關系分析. 上海口腔醫學, 2025, 34(1): 32-37..
25.	Girshick R. Fast R-CNN//Proceedings of the IEEE International Conference on Computer Vision (ICCV), Santiago: IEEE, 2015: 1440-1448..
26.	Qiu Z, Yao T, Ngo C W, et al. MLP-3D: a MLP-like 3D architecture with grouped time mixing//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans: IEEE, 2022: 3062-3072..
27.	Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need//Advances in Neural Information Processing Systems (NeurIPS), Long Beach: Curran Associates, 2017: 5998-6008..
28.	Li J, Cheng B, Niu N, et al. A fine-grained orthodontics segmentation model for 3D intraoral scan data. Comput Biol Med, 2024, 168: 107821..
29.	Kipf T N, Welling M. Semi-supervised classification with graph convolutional networks//Proceedings of the International Conference on Learning Representations (ICLR), Toulon: ICLR, 2017: 2713-2726..
30.	Bronstein M M, Bruna J, LeCun Y, et al. Geometric deep learning: going beyond Euclidean data. IEEE Signal Process Mag, 2017, 34(4): 18-42..
31.	Liu Z, Lin Y, Cao Y, et al. Swin Transformer: hierarchical vision transformer using shifted windows//Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), Montreal: IEEE, 2021: 10012-10022..
32.	Han K, Wang Y, Chen H, et al. A survey on vision transformer. IEEE Trans Pattern Anal Mach Intell, 2023, 45(1): 87-110..
33.	Gulati A, Qin J, Chiu C C, et al. Conformer: convolution-augmented transformer for speech recognition. arXiv, 2020: 2005.08100..
34.	Dong Z, Chen J Z. Transformer-based tooth alignment prediction with occlusion and collision constraints//Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), Montreal: IEEE, 2025: 25145-25154..
35.	Qi C R, Su H, Mo K, et al. PointNet: deep learning on point sets for 3D classification and segmentation//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu: IEEE, 2017: 652-660..
36.	Qiu L D, Ye C J, Chen P, et al. DArch: dental arch prior-assisted 3D tooth instance segmentation with weak annotations//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans: IEEE, 2022: 20752-20761..
37.	Al-Harbi S, AlKofide E A, AlMadi A. Mathematical analyses of dental arch curvature in normal occlusion. Angle Orthod, 2008, 78(2): 281-287..
38.	Tamayo-Quintero J D, Gómez-Mendoza J B, Guevara-Pérez S V. DentalArch: AI-based arch shape detection in orthodontics. Appl Sci, 2024, 14(6): 2567..
39.	Yang X, Zhou Y, Zhang G, et al. The KFIoU loss for rotated object detection//Proceedings of the International Conference on Learning Representations (ICLR), Kigali: ICLR, 2023: 5289-5305..
40.	杭州測度科技有限公司. 3D牙齒數據集. (2024-09-12)[2025-09-30]. https://www.scidb.cn/detail?dataSetId=2190905afcc6484ca55778065dcb1d9f&version=V1..
41.	Qi C R, Yi L, Su H, et al. PointNet++: deep hierarchical feature learning on point sets in a metric space//Advances in Neural Information Processing Systems (NeurIPS), Long Beach: Curran Associates, 2017: 6265-6274..
42.	Reddi S, Kale S, Kumar S. On the convergence of Adam and beyond//Proceedings of the International Conference on Learning Representations (ICLR), Canada: ICLR, 2018: 239-261..
43.	Loshchilov I, Hutter F. SGDR: stochastic gradient descent with warm restarts//Proceedings of the International Conference on Learning Representations (ICLR), Toulon: ICLR, 2017: 1769-1784..
44.	Kosiorek A R, Strathmann H, Zoran D, et al. NeRF-VAE: a geometry aware 3D scene generative model//International Conference on Machine Learning (ICML), Toronto: PMLR, 2021: 5742-5752..
45.	Fan Y, Wei G, Liu W J, et al. A dynamic arrangement framework for automatic tooth alignment based on orthodontic rules. Comput Aided Geom Des, 2025, 119: 102436..

1. 王興. 中國口腔醫學的發展. 中國實用口腔科雜志, 2008, 1(4): 193-195..
2. 陳佳懿, 章燕珍. 正畸治療與顳下頜關節問題的研究進展. 臨床醫學進展, 2025, 15(4): 1473-1478..
3. 胡煒, 傅民魁. 口腔正畸治療要點Ⅳ. 正畸治療中的口腔健康教育和衛生保健. 中華口腔醫學雜志, 2006, 41(5): 313-315..
4. Litjens G, Kooi T, Bejnordi B E, et al. A survey on deep learning in medical image analysis. Med Image Anal, 2017, 42(1): 60-88..
5. Huang C X, Wang J J, Wang S H, et al. A review of deep learning in dentistry. Neurocomputing, 2023, 554: 126629..
6. Yu H, Liu L D, Chen S M, et al. Collision-free path planning method for digital orthodontic treatment. Graph Models, 2025, 141: 101297..
7. 楊航, 陳瑞, 安仕鵬. 深度學習背景下的圖像三維重建技術進展綜述. 中國圖象圖形學報, 2023, 28(8): 2396-2409..
8. 李占利, 付敬鼎, 李洪安, 等. 虛擬正畸治療中的錯位牙齒自動排列方法. 圖學學報, 2019, 40(2): 225-232..
9. 陶樂然, 張容斌, 林郁欣, 等. 深度學習在牙頜面畸形診療中的應用及研究進展. 中國口腔頜面外科雜志, 2022, 20(4): 405-409..
10. 徐琦, 張曉蓉. 牙周正畸治療的研究進展. 臨床口腔醫學雜志, 2014, 28(4): 249-251..
11. 胡夢杰, 方宇航, 秦緒佳, 等. 基于網格特征的自動排牙方法. 中國機械工程, 2025, 36(11): 2738-2746..
12. 李笙銀, 周建萍. 深度學習在正畸診斷和治療中的應用進展. 臨床醫學進展, 2025, 15(9): 1577-1585..
13. Lee M K, Allareddy V, Rampa S, et al. Applications and challenges of implementing artificial intelligence in orthodontics: a primer for orthodontists. Semin Orthod, 2024, 30(1): 72-76..
14. Cui Z, Fang Y, Mei L, et al. A fully automatic AI system for tooth and alveolar bone segmentation from cone-beam CT images. Nat Commun, 2022, 13(1): 2096..
15. Yang L C, Shi Z F, Wu Y Q, et al. iOrthopredictor: model-guided deep prediction of teeth alignment. ACM Trans Graph, 2020, 39(6): 216..
16. Li X S, Bi L, Kim J M, et al. Malocclusion treatment planning via PointNet-based spatial transformation network//Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), Lima: Springer, 2020: 105-114..
17. Wei G D, Cui Z M, Liu Y M, et al. TANet: towards fully automatic tooth arrangement//Computer Vision-ECCV 2020: 16th European Conference (ECCV), Glasgow: Springer, 2020: 481-497..
18. Wang Y, Sun Y, Liu Z, et al. Dynamic graph CNN for learning on point clouds. ACM Trans Graph, 2019, 38(5): 146..
19. Deng Q, Yang X, Huang M, et al. TAPoseNet: teeth alignment based on pose estimation via multi-scale graph convolutional network//Medical Image Computing and Computer Assisted Intervention (MICCAI), Marrakesh: Springer, 2024: 314-323..
20. Zheng Y, Chen B, Shen Y, et al. TeethGNN: semantic 3D teeth segmentation with graph neural networks. IEEE Trans Vis Comput Graph, 2023, 29(7): 3158-3168..
21. Dosovitskiy A, Beyer L, Kolesnikov A, et al. An image is worth 16x16 words: transformers for image recognition at scale. arXiv, 2020: 2010.11929..
22. Lei C, Xia M, Wang S, et al. Automatic tooth arrangement with joint features of point and mesh representations via diffusion probabilistic models. Comput Aided Geom Des, 2024, 111: 102293..
23. 白煜, 高盟, 劉冬梅, 等. 骨性Ⅱ類錯牙合下頜基骨與牙弓形態的研究. 口腔醫學, 2025, 45(6): 436-439..
24. 束傳亮, 江煜, 蔡佳. 成年女性上頜前牙美學區牙列擁擠度與基骨形態、牙弓和牙槽弓形態關系分析. 上海口腔醫學, 2025, 34(1): 32-37..
25. Girshick R. Fast R-CNN//Proceedings of the IEEE International Conference on Computer Vision (ICCV), Santiago: IEEE, 2015: 1440-1448..
26. Qiu Z, Yao T, Ngo C W, et al. MLP-3D: a MLP-like 3D architecture with grouped time mixing//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans: IEEE, 2022: 3062-3072..
27. Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need//Advances in Neural Information Processing Systems (NeurIPS), Long Beach: Curran Associates, 2017: 5998-6008..
28. Li J, Cheng B, Niu N, et al. A fine-grained orthodontics segmentation model for 3D intraoral scan data. Comput Biol Med, 2024, 168: 107821..
29. Kipf T N, Welling M. Semi-supervised classification with graph convolutional networks//Proceedings of the International Conference on Learning Representations (ICLR), Toulon: ICLR, 2017: 2713-2726..
30. Bronstein M M, Bruna J, LeCun Y, et al. Geometric deep learning: going beyond Euclidean data. IEEE Signal Process Mag, 2017, 34(4): 18-42..
31. Liu Z, Lin Y, Cao Y, et al. Swin Transformer: hierarchical vision transformer using shifted windows//Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), Montreal: IEEE, 2021: 10012-10022..
32. Han K, Wang Y, Chen H, et al. A survey on vision transformer. IEEE Trans Pattern Anal Mach Intell, 2023, 45(1): 87-110..
33. Gulati A, Qin J, Chiu C C, et al. Conformer: convolution-augmented transformer for speech recognition. arXiv, 2020: 2005.08100..
34. Dong Z, Chen J Z. Transformer-based tooth alignment prediction with occlusion and collision constraints//Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), Montreal: IEEE, 2025: 25145-25154..
35. Qi C R, Su H, Mo K, et al. PointNet: deep learning on point sets for 3D classification and segmentation//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu: IEEE, 2017: 652-660..
36. Qiu L D, Ye C J, Chen P, et al. DArch: dental arch prior-assisted 3D tooth instance segmentation with weak annotations//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans: IEEE, 2022: 20752-20761..
37. Al-Harbi S, AlKofide E A, AlMadi A. Mathematical analyses of dental arch curvature in normal occlusion. Angle Orthod, 2008, 78(2): 281-287..
38. Tamayo-Quintero J D, Gómez-Mendoza J B, Guevara-Pérez S V. DentalArch: AI-based arch shape detection in orthodontics. Appl Sci, 2024, 14(6): 2567..
39. Yang X, Zhou Y, Zhang G, et al. The KFIoU loss for rotated object detection//Proceedings of the International Conference on Learning Representations (ICLR), Kigali: ICLR, 2023: 5289-5305..
40. 杭州測度科技有限公司. 3D牙齒數據集. (2024-09-12)[2025-09-30]. https://www.scidb.cn/detail?dataSetId=2190905afcc6484ca55778065dcb1d9f&version=V1..
41. Qi C R, Yi L, Su H, et al. PointNet++: deep hierarchical feature learning on point sets in a metric space//Advances in Neural Information Processing Systems (NeurIPS), Long Beach: Curran Associates, 2017: 6265-6274..
42. Reddi S, Kale S, Kumar S. On the convergence of Adam and beyond//Proceedings of the International Conference on Learning Representations (ICLR), Canada: ICLR, 2018: 239-261..
43. Loshchilov I, Hutter F. SGDR: stochastic gradient descent with warm restarts//Proceedings of the International Conference on Learning Representations (ICLR), Toulon: ICLR, 2017: 1769-1784..
44. Kosiorek A R, Strathmann H, Zoran D, et al. NeRF-VAE: a geometry aware 3D scene generative model//International Conference on Machine Learning (ICML), Toronto: PMLR, 2021: 5742-5752..
45. Fan Y, Wei G, Liu W J, et al. A dynamic arrangement framework for automatic tooth alignment based on orthodontic rules. Comput Aided Geom Des, 2025, 119: 102436..

Journal of Biomedical Engineering

Research on automated orthodontic tooth arrangement by integrating multi-source structural information and dental arch prediction

Abstract Full text Figures/Tables Video References Cited by

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