Precise tooth design using deep learning-based templates

• Published in: Journal of dentistry Vol. 144; p. 104971
• Main Authors: Chen, Du, Yu, Mei-Qi, Li, Qi-Jing, He, Xiang, Liu, Fei, Shen, Jie-Fei
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 01.05.2024
• Subjects: Artificial Intelligence; Cad; Computer-Aided Design; Deep Learning; Dental anatomy; Dental Prosthesis Design - methods; Esthetic dentistry; Humans; Incisor - anatomy & histology; Maxilla - anatomy & histology; Models, Dental; Neural Networks, Computer; Prosthetic dentistry/prosthodontics; Esthetic dentistry; Prosthetic dentistry/prosthodontics; Dental anatomy; Artificial Intelligence; Cad
• ISSN: 0300-5712; 1879-176X
• DOI: 10.1016/j.jdent.2024.104971

•Deep learning customizes highly personalized shape templates for defective teeth.•Deep learning enhances the efficiency and accuracy of digital restorative design.•The morphology generated based on implicit templates is more stable and reliable. In prosthodontic procedures, traditional computer-aided design (CAD) is often time-consuming and lacks accuracy in shape restoration. In this study, we combined implicit template and deep learning (DL) to construct a precise neural network for personalized tooth defect restoration. Ninety models of right maxillary central incisor (80 for training, 10 for validation) were collected. A DL model named ToothDIT was trained to establish an implicit template and a neural network capable of predicting unique identifications. In the validation stage, teeth in validation set were processed into corner, incisive, and medium defects. The defective teeth were inputted into ToothDIT to predict the unique identification, which actuated the deformation of the implicit template to generate the highly customized template (DIT) for the target tooth. Morphological restorations were executed with templates from template shape library (TSL), average tooth template (ATT), and DIT in Exocad (GmbH, Germany). RMSestimate, width, length, aspect ratio, incisal edge curvature, incisive end retraction, and guiding inclination were introduced to assess the restorative accuracy. Statistical analysis was conducted using two-way ANOVA and paired t-test for overall and detailed differences. DIT displayed significantly smaller RMSestimate than TSL and ATT. In 2D detailed analysis, DIT exhibited significantly less deviations from the natural teeth compared to TSL and ATT. The proposed DL model successfully reconstructed the morphology of anterior teeth with various degrees of defects and achieved satisfactory accuracy. This approach provides a more reliable reference for prostheses design, resulting in enhanced accuracy in morphological restoration. This DL model holds promise in assisting dentists and technicians in obtaining morphology templates that closely resemble the original shape of the defective teeth. These customized templates serve as a foundation for enhancing the efficiency and precision of digital restorative design for defective teeth.