ImplantFormer: vision transformer-based implant position regression using dental CBCT data

Xinquan Yang, Xuguang Li, Xuechen Li, Peixi Wu, Linlin Shen, Yongqiang Deng

Research output: Journal PublicationArticlepeer-review

Abstract

Implant prosthesis is the most appropriate treatment for dentition defect or dentition loss, which usually involves a surgical guide design process to decide the implant position. However, such design heavily relies on the subjective experiences of dentists. In this paper, a transformer-based Implant Position Regression Network, ImplantFormer, is proposed to automatically predict the implant position based on the oral CBCT data. We creatively propose to predict the implant position using the 2D axial view of the tooth crown area and fit a centerline of the implant to obtain the actual implant position at the tooth root. Convolutional stem and decoder are designed to coarsely extract image features before the operation of patch embedding and integrate multi-level feature maps for robust prediction, respectively. As both long-range relationship and local features are involved, our approach can better represent global information and achieves better location performance. Extensive experiments on a dental implant dataset through fivefold cross-validation demonstrated that the proposed ImplantFormer achieves superior performance than existing methods.

Original languageEnglish
Pages (from-to)6643-6658
Number of pages16
JournalNeural Computing and Applications
Volume36
Issue number12
DOIs
Publication statusPublished - Apr 2024
Externally publishedYes

Keywords

  • Deep learning
  • Dental implant
  • Implant prosthesis
  • Vision transformer

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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