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Tooth3dNet: A preliminary exploration for automatic 3D morphology design of dental crowns with a deep generative
Shunv Ying1, Feng Huang2, Jiaxing Yin2
1Stomatology Hospital, School of Stomatology, Zhejiang University School of Medicine, Zhejiang Provincial Clinical Research Center for Oral Diseases, Key Laboratory of Oral Biomedical Research of Zhejiang Province, Cancer Center of Zhejiang University, Engineering Research Center of Oral Biomaterials and Devices of Zhejiang Province, Hangzhou 310000, China.
Objectives:
Artificial intelligence is opening new avenues for digital dental restoration. To advance the automatic design of dental crowns, this study proposed a novel deep generative network to enable automatic generation of 3D crown shapes for various types of missing teeth-from incisors to molars-based solely on the surrounding oral environment.
Methods:
The proposed network, named Tooth3dNet, adopted a Transformer-based encoder-decoder architecture, featuring specially designed optimized query generation and a critical incremental point cloud reconstruction module. It was trained directly on a self-created large-scale 3D intraoral scan point cloud dataset. ANOVA and paired t-test statistical analysis were implemented to compare the performance of the proposed network and other state-of-the-art methods.
Results:
Multi-metric evaluations showed that our network achieved a mean reconstruction error of 0.200 mm and a Hausdorff distance of 0.683 mm, outperformed current state-of-the-art point cloud generation networks significantly (P < 0.05). The generated point clouds produced reconstructed surfaces that retained the anatomical features of the teeth.
Conclusions:
Our method demonstrates good performance in generating primary 3D morphology of dental crowns.
Clinical Significance:
Although the present method is limited to occlusal surface generation and some areas of the proximal surfaces, it introduces a novel approach for crown design and lays the groundwork for future development of fully automated, full-crown morphology generation.
