Tooth generative adversarial network: Anatomical optimisation using Wasserstein generative adversarial network for tooth generation hyphenated dental 3-dimensional precision printing

Objectives
Deep learning (DL) has been applied to reconstruct missing tooth surfaces. Although promising, no current method ensures that DL-generated prosthesis simultaneously meet clinical requirements for accuracy, surface roughness, anatomical morphology, and mechanical properties across fabrication techniques. Furthermore, while both natural tooth and technician-designed prosthesis datasets are available, there has been no research on how to better use these two datasets. The purpose of this study is to address these issues.
Methods
We developed a geometric processing method that combines modified Delaunay triangulation (DT) reconstruction to achieve accurate, mechanically suitable results from 256 × 256 depth maps. A Tooth Generative Adversarial Network (ToothGAN) was trained with specialized loss functions for anatomical features and smoothness using both natural and technician-designed datasets. The output was validated via 3D printing and in vitro testing.
Results
ToothGAN outperformed prior algorithms on natural tooth data across metrics including Root Mean Square Error (RMSE), Structural Similarity Index (SSIM), 3-Dimensional Route Mean Square Error (3DRMSE), and Visual Assessment (VA) score. The generated crowns met the mechanical standards such as roughness, and Sharp Mesh Corner Ratio (SMCR), making them suitable for precision 3-Dimensional manufacturing. Blending natural and technician-designed data improved learning of anatomical features like cusps and grooves, though some metrics such as groove distance and occlusal contact points were altered.
Conclusions
ToothGAN satisfies precision manufacturing demands and shows strong potential for clinical application in crown generation.