Precise tooth design using deep learning-based templates.

Journal: Journal of dentistry
PMID: 38548165

Abstract

OBJECTIVES: In prosthodontic procedures, traditional computer-aided design (CAD) is often time-consuming and lacks accuracy in shape restoration. In this study, we combined implicit template and deep learning (DL) to construct a precise neural network for personalized tooth defect restoration.

Authors

  • Du Chen
    State Key Laboratory of Oral Diseases, National Clinical Research Center for Oral Diseases, National Center for Stomatology, West China School of Stomatology, Sichuan University, Chengdu 610041, PR China; Department of Prosthodontics, West China Hospital of Stomatology, Sichuan University, Chengdu 610041, PR China.
  • Mei-Qi Yu
    State Key Laboratory of Oral Diseases, National Clinical Research Center for Oral Diseases, National Center for Stomatology, West China School of Stomatology, Sichuan University, Chengdu 610041, PR China; Department of Prosthodontics, West China Hospital of Stomatology, Sichuan University, Chengdu 610041, PR China.
  • Qi-Jing Li
    State Key Laboratory of Oral Diseases, National Clinical Research Center for Oral Diseases, National Center for Stomatology, West China School of Stomatology, Sichuan University, Chengdu 610041, PR China; Department of Prosthodontics, West China Hospital of Stomatology, Sichuan University, Chengdu 610041, PR China.
  • Xiang He
    Department of Dermatology, Shuguang Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China.
  • Fei Liu
    Department of Interventional Radiology, Qinghai Red Cross Hospital, Xining, Qinghai, China.
  • Jie-Fei Shen
    State Key Laboratory of Oral Diseases, National Clinical Research Center for Oral Diseases, National Center for Stomatology, West China School of Stomatology, Sichuan University, Chengdu 610041, PR China; Department of Prosthodontics, West China Hospital of Stomatology, Sichuan University, Chengdu 610041, PR China. Electronic address: shenjiefei@scu.edu.cn.

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