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Automated reconstruction of missing tooth morphology using a transformer-based implicit neural network: A multi-tooth
Yiqing Wang1, Yuze Shi2, Nan Li2
1Department of Prosthodontics, Peking University School and Hospital of Stomatology, National Center of Stomatology, National Clinical Research Center for Oral Diseases, National Engineering Research Center of Oral Biomaterials and Digital Medical Devices, Beijing Key Laboratory of Digital Stomatology, Research Center of Engineering and Technology for Computerized Dentistry, Ministry of Health, NMPA Key Laboratory for Dental Materials, Beijing, PR China.
A new transformer-based model accurately reconstructs missing tooth morphology, showing high fidelity and dimensional consistency for digital dental restorations. This automated approach enhances efficiency in dental workflows across various tooth positions.
Area of Science:
- Artificial Intelligence in Dentistry
- Computational Dental Anatomy
- Digital Dentistry
Background:
- Automated tooth morphology reconstruction is crucial for digital dental restorations.
- Existing methods may lack accuracy or generalizability across diverse tooth types.
Purpose of the Study:
- Develop a novel transformer-based implicit neural network (INN) model for automated tooth morphology reconstruction.
- Evaluate the model's accuracy and generalizability across different tooth positions (molars, premolars, incisors).
Main Methods:
- Utilized digital full-arch casts of molars, premolars, and incisors.
- Developed a transformer-based INN model with a self-structure enhancement module and multi-view 2D depth maps.
- Assessed performance using chamfer distance, F-score, and volumetric intersection over union; compared reconstructed crowns (GC) with original (OC) and technician-designed (TC) crowns using RMS and dimensional analysis.
Main Results:
- The model trained with 50,000 sampling points demonstrated superior reconstruction accuracy, closely matching natural tooth morphology.
- Central incisors achieved the highest accuracy (CD = 0.0028 × 10-2, F-score = 0.9670, IoU = 0.9716).
- Reconstructed crowns showed comparable deviations to technician-designed crowns in molars, but higher deviations in premolars and incisors; dimensional analysis revealed close matches to original crowns across all tooth types.
Conclusions:
- The transformer-based model effectively reconstructs missing single-tooth morphology with acceptable accuracy and adaptability.
- The model's high fidelity and dimensional consistency show potential for improving digital dental restoration workflows.
- Further research should focus on expanding datasets, refining the model, and incorporating clinical validations.
