CDSNet: An automated method for assessing growth stages from various anatomical regions in lateral cephalograms based on deep learning.

Journal: Journal of the World federation of orthodontists
Published Date: Nov 22, 2024

Abstract

BACKGROUND: The assessment of growth stages, typically determined by Cervical Vertebrae Maturation (CVM), plays a crucial role in orthodontics. However, there is a potential deviation from actual growth stages when using CVM. This study aimed to introduce CDSNet, an interpretable deep learning model for assessing growth stages based on cervical vertebrae, dentition, and frontal sinus in lateral cephalograms.

Authors

  • Yuchen Zhang
    School of Computer Science, Shaanxi Normal University, Xi'an, China.
  • Zhen Lu
    School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, China.
  • Jianglin Zhou
    Bioinformatics Center of AMMS, Beijing, China.
  • Yi Sun
    Department of Environmental and Occupational Health, Program in Public Health, University of California, Irvine, CA, USA.
  • Wuci Yi
    Key Laboratory of Shaanxi Province for Craniofacial Precision Medicine Research, College of Stomatology, Xi'an Jiaotong University, Xi'an, China.
  • Juan Wang
    Institute of Electrical Engineering, Yanshan University, Qinhuangdao, China.
  • Tianjing Du
    Key Laboratory of Shaanxi Province for Craniofacial Precision Medicine Research, College of Stomatology, Xi'an Jiaotong University, Xi'an, China.
  • Dongning Li
    Key Laboratory of Shaanxi Province for Craniofacial Precision Medicine Research, College of Stomatology, Xi'an Jiaotong University, Xi'an, China.
  • Xinyan Zhao
    School of Information, University of Michigan, Ann Arbor, Michigan, USA.
  • Yifei Xu
    Gansu Institute of Food Inspection, Lanzhou, China.
  • Chen Li
    School of Computer Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, China.
  • Kun Qi
    College of Pharmaceutical Sciences, Zhejiang University, 866 Yuhangtang Road, Xihu District, Hangzhou, 310058, China.

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