Automatic restoration and reconstruction of defective tooth based on deep learning technology

Abstract

Background: Accurate restoration and reconstruction of tooth morphology are crucial in restorative dentistry, implantology, and forensic odontology. Traditional methods, like manual wax modeling and template-based computer-aided design (CAD), struggle with accuracy, personalization, and efficiency. To address the challenge, we propose an innovative and efficient deep learning-based framework designed for the automatic restoration and reconstruction of tooth morphology. Methods: The proposed method contains three stages. Firstly, an RGB image of a defective tooth is inputted into the restoration network, which fills in the missing regions to produce a complete RGB image of the tooth. The resulting image is then converted to a grayscale image in the preprocessing stage to ensure compatibility with the subsequent reconstruction process. Finally, the 3D reconstruction network utilizes the grayscale image to generate a detailed 3D mesh model of the tooth. Results: The experimental results demonstrate that the proposed method achieves superior performance in restoration quality, reconstruction accuracy, generalization, and inference speed, with an average time of 12 s per image. Notably, compared to the original Pixel2Mesh, the improved ResNet50-based Pixel2Mesh enhances the average F-Score, CD, and EMD for reconstructed tooth models by 26.5%, 34.7%, and 22.3%, respectively. Conclusions: The approach proposed in this paper offers a promising solution for personalized intelligent, and efficient tooth restoration and reconstruction, providing a valuable tool for dental diagnostics and treatment planning.