Autologous Transplantation Tooth Guide Design Based on Deep Learning.

Journal: Journal of oral and maxillofacial surgery : official journal of the American Association of Oral and Maxillofacial Surgeons
PMID: 37832596

Abstract

BACKGROUND: Autologous tooth transplantation requires precise surgical guide design, involving manual tracing of donor tooth contours based on patient cone-beam computed tomography (CBCT) scans. While manual corrections are time-consuming and prone to human errors, deep learning-based approaches show promise in reducing labor and time costs while minimizing errors. However, the application of deep learning techniques in this particular field is yet to be investigated.

Authors

  • Lifen Wei
    Department of Dental Implantation, Affiliated Stomatology Hospital of Guangzhou Medical University, Guangdong Engineering Research Center of Oral Restoration and Reconstruction, Guangzhou Key Laboratory of Basic and Applied Research of Oral Regenerative Medicine, Guangzhou, Guangdong, China.
  • Shuyang Wu
    Department of Pathology, Collaborative Innovation Center for Cancer Medicine, State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, China.
  • Zelun Huang
    Department of Dental Implantation, Affiliated Stomatology Hospital of Guangzhou Medical University, Guangdong Engineering Research Center of Oral Restoration and Reconstruction, Guangzhou Key Laboratory of Basic and Applied Research of Oral Regenerative Medicine, Guangzhou, Guangdong, China.
  • Yaxin Chen
    Department of Pharmacy, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200240, China.
  • Haoran Zheng
    School of Computer Science and Technology, University of Science and Technology of China, Huangshan Road, Hefei, 230026, People's Republic of China. zhulx@mail.ustc.edu.cn.
  • Liping Wang
    School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200011, China.

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