Text-Conditioned Multi-Expert Regression Framework for Fully Automated Multi-Abutment Design
Dental implant abutments serve as the geometric and biomechanical interface between the implant fixture and the prosthetic crown, yet their design relies heavily on manual effort and is time-consuming. Although deep neural networks have been proposed to assist dentists in designing abutments, most existing approaches remain largely manual or semi-automated, requiring substantial clinician intervention and lacking scalability in multi-abutment scenarios. To address these limitations, we propose TEMAD, a fully automated, text-conditioned multi-expert architecture for multi-abutment design. This framework integrates implant site localization and implant system, compatible abutment parameter ...
| Authors | Mianjie Zheng, Xinquan Yang, Xuefen Liu et al. |
|---|---|
| Primary category | cs.CV |
| Published | 10 April 2026 |
| Last updated at source | 10 April 2026 |
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