Convolutional neural network-assisted diagnosis of midpalatal suture maturation stage in cone-beam computed tomography.

Journal: Journal of dentistry
PMID: 38101505

Abstract

OBJECTIVES: The selection of treatment for maxillary expansion is closely related to the calcification degree of the midpalatal suture. A classification method for individual assessment of the morphology of midpalatal suture in cone-beam computed tomography (CBCT) is useful for evaluating the calcification degree. Currently, convolutional neural networks (CNNs) have been introduced into the field of oral and maxillofacial imaging diagnosis. This study validated the ability of CNN models in assessing the maturation stage of the midpalatal suture.

Authors

  • Mengyao Zhu
    Department of Orthodontics, School of Stomatology, Capital Medical University, Beijing 100050, China.
  • Pan Yang
    Department of Oral and Maxillofacial Radiology, Beijing Stomatology Hospital, School of Stomatology, Capital Medical University, Beijing, China.
  • Ce Bian
    Division of Colorectal Surgery, Changzheng Hospital, Navy Medical University, Shanghai, China.
  • Feifei Zuo
    LargeV Instrument Corp., Ltd., Beijing 100084, China.
  • Zhongmin Guo
    Beijing Information Science and Technology University, Beijing, China. Electronic address: youwan@bistu.edu.cn.
  • Yufeng Wang
    People's Hospital of Gaoxin, 768 Fudong Road, Weifang 261205, China.
  • Yajie Wang
    Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China.
  • Yuxing Bai
    Department of Orthodontics, School of Stomatology, Capital Medical University, Beijing 100050, China.
  • Ning Zhang
    Institute of Nuclear Agricultural Sciences, Zhejiang University, Hangzhou, 310058, China.

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