Text Condition Embedded Regression Network for Automated Dental Abutment Design

TL;DR

This paper introduces TCEAD, a novel AI framework that uses text-guided localization and self-supervised learning to automate and improve the accuracy of dental abutment design from oral scans.

Contribution

The study presents a new text condition embedded regression network that enhances abutment localization and design accuracy using a combination of CLIP and MeshMAE techniques.

Findings

TCEAD outperforms existing methods with up to 12.85% higher IoU.

The framework effectively localizes abutment areas using text descriptions.

Pre-training on oral scan data improves feature extraction for abutment design.

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.