Text-Conditioned Multi-Expert Regression Framework for Fully Automated Multi-Abutment Design

• URL: http://arxiv.org/abs/2604.09047v1
• Date: Fri, 10 Apr 2026 07:14:36 GMT
• Title: Text-Conditioned Multi-Expert Regression Framework for Fully Automated Multi-Abutment Design
• Abstract summary: TEMAD is a fully automated, text-conditioned multi-expert architecture for multi-abutment design.<n>This framework integrates implant site localization and implant system, compatible abutment parameter regression into a unified pipeline.<n> experiments on a large-scale abutment design dataset show that TEMAD achieves state-of-the-art performance compared to existing methods.
• Score: 40.12466032497699
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 regression into a unified pipeline. Specifically, we introduce an Implant Site Identification Network (ISIN) to automatically localize implant sites and provide this information to the subsequent multi-abutment regression network. We further design a Tooth-Conditioned Feature-wise Linear Modulation (TC-FiLM) module, which adaptively calibrates mesh representations using tooth embeddings to enable position-specific feature modulation. Additionally, a System-Prompted Mixture-of-Experts (SPMoE) mechanism leverages implant system prompts to guide expert selection, ensuring system-aware regression. Extensive experiments on a large-scale abutment design dataset show that TEMAD achieves state-of-the-art performance compared to existing methods, particularly in multi-abutment settings, validating its effectiveness for fully automated dental implant planning.

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