Text Condition Embedded Regression Network for Automated Dental Abutment Design

• URL: http://arxiv.org/abs/2511.22578v1
• Date: Thu, 27 Nov 2025 16:08:10 GMT
• Title: Text Condition Embedded Regression Network for Automated Dental Abutment Design
• Authors: Mianjie Zheng, Xinquan Yang, Xuguang Li, Xiaoling Luo, Xuefen Liu, Kun Tang, He Meng, Linlin Shen,
• Abstract summary: Long-term use of inappropriate dental implant abutments may result in implant complications, including peri-implantitis.<n>Using artificial intelligence to assist dental implant abutment design can quickly improve the efficiency of abutment design and enhance abutment adaptability.<n>We propose a text condition embedded abutment design framework (TCEAD), the novel automated abutment design solution available in literature.
• Score: 40.65156494921462
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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