Text Condition Embedded Regression Network for Automated Dental Abutment Design

2025-11-27

AI Summary

Current abutment design for dental implants is time-consuming, heavily experience-dependent, and prone to peri-implantitis due to poor anatomical fit. To address these challenges, this paper proposes Text-Conditioned Embedding for Automated Abutment Design (TCEAD), the first framework integrating natural language descriptions for abutment region localization. TCEAD leverages the CLIP text encoder to enhance fine-grained anatomical structure perception and synergistically combines a text-guided localization module with a pre-trained geometric encoder derived from MeshMAE—a self-supervised 3D mesh modeling framework. Evaluated on a large-scale clinical dataset, TCEAD achieves IoU improvements of 0.8–12.85% over state-of-the-art methods, demonstrating significant gains in both design efficiency and personalized anatomical adaptation accuracy.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Sentence-level Semantics, Textual Inference, etc.
• Computer Vision: 3D Computer Vision

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

The abutment is an important part of artificial dental implants, whose design process is time-consuming and labor-intensive. Long-term use of inappropriate dental implant abutments may result in implant complications, including peri-implantitis. Using artificial intelligence to assist dental implant abutment design can quickly improve the efficiency of abutment design and enhance abutment adaptability. In this paper, we propose a text condition embedded abutment design framework (TCEAD), the novel automated abutment design solution available in literature. The proposed study extends the self-supervised learning framework of the mesh mask autoencoder (MeshMAE) by introducing a text-guided localization (TGL) module to facilitate abutment area localization. As the parameter determination of the abutment is heavily dependent on local fine-grained features (the width and height of the implant and the distance to the opposing tooth), we pre-train the encoder using oral scan data to improve the model's feature extraction ability. Moreover, considering that the abutment area is only a small part of the oral scan data, we designed a TGL module, which introduces the description of the abutment area through the text encoder of Contrastive Language-Image Pre-training (CLIP), enabling the network to quickly locate the abutment area. We validated the performance of TCEAD on a large abutment design dataset. Extensive experiments demonstrate that TCEAD achieves an Intersection over Union (IoU) improvement of 0.8%-12.85% over other mainstream methods, underscoring its potential in automated dental abutment design.

Problem

• Automates dental abutment design to reduce time and labor
• Improves abutment adaptability to prevent implant complications
• Uses text-guided localization for precise area identification

Innovation

• Text-guided localization module for abutment area identification
• Self-supervised mesh autoencoder pre-trained on oral scan data
• CLIP text encoder integration to enhance feature extraction