Automated reconstruction of missing tooth morphology using a transformer‐based implicit neural network: A multi‐tooth position evaluation
The proposed transformer-based model effectively achieved automated reconstruction of missing single-tooth morphology with acceptable accuracy and adaptability across different tooth positions. Its high fidelity and dimensional consistency highlight its potential for improving efficiency in digital dental restoration workflows. Further studies are warranted to expand dataset diversity, refine the model architecture, and incorporate clinical and functional validations.
Authors
- Yiqing Wang
- Li Chen (ORCID: https://orcid.org/0000-0001-9294-4372)
- Wei‐Shao Lin (ORCID: https://orcid.org/0000-0002-4881-0569)
- Jianguo Tan (ORCID: https://orcid.org/0000-0002-4312-9184)
- Nan Li (ORCID: https://orcid.org/0000-0002-8261-1352)
- Yaozeng Shi (ORCID: https://orcid.org/0009-0007-2081-2566)
Institutions
- Beijing Technology and Business University (CN)
- National Clinical Research (US)
- Indiana University – Purdue University Indianapolis (US)
Publication Details
- Journal
- Journal of Prosthodontics
- Published
- 2025-12-19
- DOI
- https://doi.org/10.1111/jopr.70084
- Primary Topic
- Dental Radiography and Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Peking University