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

• URL: http://arxiv.org/abs/2512.11507v1
• Date: Fri, 12 Dec 2025 12:08:05 GMT
• Title: SSA3D: Text-Conditioned Assisted Self-Supervised Framework for Automatic Dental Abutment Design
• Authors: Mianjie Zheng, Xinquan Yang, Along He, Xuguang Li, Feilie Zhong, Xuefen Liu, Kun Tang, Zhicheng Zhang, Linlin Shen,
• Abstract summary: We propose a Self-supervised assisted automatic abutment design framework (SS$A3$D), which employs a dual-branch architecture with a reconstruction branch and a regression branch.<n>The regression branch then predicts the abutment parameters under supervised learning, which eliminates the separate pre-training and fine-tuning process.<n>It also achieves state-of-the-art performance compared to other methods, significantly improving the accuracy and efficiency of automated abutment design.
• Score: 52.57094737117145
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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