SSA3D: Text-Conditioned Assisted Self-Supervised Framework for Automatic Dental Abutment Design

2025-12-12

AI Summary

Dental abutment design is labor-intensive, highly experience-dependent, and hindered by scarce annotated clinical data, resulting in slow progress in AI-driven automation. To address this, we propose a self-supervised auxiliary framework that eliminates the need for large-scale manual annotations. Our method integrates intraoral scanning-based 3D reconstruction with a dual-branch architecture for parametric regression, enabling end-to-end abutment parameter prediction. We introduce a novel self-supervised paradigm that requires neither pretraining nor fine-tuning, incorporating a Text-Conditioned Prompt (TCP) module to dynamically inject clinical semantics—such as implant position and model—into the network, thereby guiding regional attention and constraining the parameter space. Leveraging joint point cloud and mesh modeling with collaborative supervision, our approach achieves superior geometric accuracy and clinical adaptability over existing self-supervised learning (SSL) and state-of-the-art (SOTA) methods on a proprietary clinical dataset, while reducing training time by 50%.

Technology Category

• Machine Learning: Unsupervised & Self-Supervised Learning
• Computer Vision: Multi-modal Vision
• Natural Language Processing: Safety and Robustness

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Search and Retrieval-Augmented AI: Agentic search

Abstract

Abutment design is a critical step in dental implant restoration. However, manual design involves tedious measurement and fitting, and research on automating this process with AI is limited, due to the unavailability of large annotated datasets. Although self-supervised learning (SSL) can alleviate data scarcity, its need for pre-training and fine-tuning results in high computational costs and long training times. In this paper, we propose a Self-supervised assisted automatic abutment design framework (SS$A^3$D), which employs a dual-branch architecture with a reconstruction branch and a regression branch. The reconstruction branch learns to restore masked intraoral scan data and transfers the learned structural information to the regression branch. The regression branch then predicts the abutment parameters under supervised learning, which eliminates the separate pre-training and fine-tuning process. We also design a Text-Conditioned Prompt (TCP) module to incorporate clinical information (such as implant location, system, and series) into SS$A^3$D. This guides the network to focus on relevant regions and constrains the parameter predictions. Extensive experiments on a collected dataset show that SS$A^3$D saves half of the training time and achieves higher accuracy than traditional SSL methods. It also achieves state-of-the-art performance compared to other methods, significantly improving the accuracy and efficiency of automated abutment design.

Problem

• Automates dental abutment design using AI to reduce manual effort
• Addresses data scarcity and high computational costs in self-supervised learning
• Incorporates clinical text information to guide and constrain parameter predictions

Innovation

• Dual-branch architecture eliminates separate pre-training and fine-tuning
• Text-Conditioned Prompt module incorporates clinical information into network
• Reconstruction branch transfers structural information to regression branch