Automatic identification of hard and soft tissue landmarks in cone-beam computed tomography via deep learning with diversity datasets: a methodological study.

Journal: BMC oral health
PMID: 40200295

Abstract

BACKGROUND: Manual landmark detection in cone beam computed tomography (CBCT) for evaluating craniofacial structures relies on medical expertise and is time-consuming. This study aimed to apply a new deep learning method to predict and locate soft and hard tissue craniofacial landmarks on CBCT in patients with various types of malocclusion.

Authors

  • Yan Jiang
    Department of Nursing/Evidence-based Nursing Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
  • Canyang Jiang
    Department of Stomatology, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, 350212, China.
  • Bin Shi
    Department of Materials Science and Engineering, University of Toronto, ON M5S 3H5, Canada. Electronic address: binmse.shi@mail.utoronto.ca.
  • You Wu
    Tsinghua University School of Medicine, Beijing, China.
  • Shuli Xing
    Center for Advanced Image and Information Technology, School of Electronics & Information Engineering, Chon Buk National University, Jeonju, Chon Buk 54896, Korea.
  • Hao Liang
    a Marine College Shandong University (weihai) , Shandong , China .
  • Jianping Huang
    School of Physics and Telecommunication Engineering, South China Normal University, Guangzhou 510006, China. Electronic address: jianping@m.scnu.edu.cn.
  • Xiaohong Huang
    The First Affiliated Hospital of Fujian Medical University, Fuzhou, China. 13905006768@139.com.
  • Li Huang
    National Research Center for Resettlement (NRCR), Hohai University, 1 Xikang Road, Nanjing 210098, China. lily8214@hhu.edu.cn.
  • Lisong Lin
    Department of Oral and Maxillofacial Surgery, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China.

Keywords

External Resources

Popular Topics
Recent Journals