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

2026-04-10

AI Summary

This study addresses the limitations of current dental implant abutment design, which relies heavily on manual intervention and struggles to scale to multi-abutment scenarios. To overcome this, the authors propose TEMAD, a novel framework that unifies implant site localization and compatible abutment parameter regression into a fully automated pipeline for the first time, leveraging text-conditioned guidance to enable collaborative multi-abutment design. Key innovations include a tooth embedding–driven, position-specific feature modulation module (TC-FiLM) and a mixture-of-experts mechanism with system-prompt–based dynamic routing (SPMoE). Evaluated on a large-scale clinical dataset, TEMAD significantly outperforms existing methods, demonstrating both the feasibility and superiority of fully automated abutment design, particularly in complex multi-abutment cases.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Computer Vision: Multi-modal Vision

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

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.

Problem

• dental implant abutment
• multi-abutment design
• automation
• scalability
• clinician intervention

Innovation

• text-conditioned
• multi-expert regression
• fully automated abutment design
• feature-wise linear modulation
• mixture-of-experts