Deep learning based quantitative cervical vertebral maturation analysis.

Journal: Head & face medicine
PMID: 40140932

Abstract

OBJECTIVES: This study aimed to enhance clinical diagnostics for quantitative cervical vertebral maturation (QCVM) staging with precise landmark localization. Existing methods are often subjective and time-consuming, while deep learning alternatives withstand the complex anatomical variations. Therefore, we designed an advanced two-stage convolutional neural network customized for improved accuracy in cervical vertebrae analysis.

Authors

  • Fulin Jiang
  • Abbas Ahmed Abdulqader
    State Key Laboratory of Oral Diseases, West China School of Stomatology, West China Hospital of Stomatology, Sichuan University, Chengdu, 610041, China.
  • Yan Yan
    Department of Biomedical Engineering, Wayne State University, Detroit, Michigan, USA.
  • Fangyuan Cheng
    Chengdu Boltzmann Intelligence Technology Co., Ltd, Chengdu, China.
  • Tao Xiang
  • Jinghong Yu
    College of Computer Science, Chongqing University, Chongqing University Three Gorges Hospital, Chongqing, 400044, China.
  • Juan Li
    Department of Hygienic Inspection, School of Public Health, Jilin University 1163 Xinmin Street Changchun 130021 Jilin China songxiuling@jlu.edu.cn li_juan@jlu.edu.cn jinmh@jlu.edu.cn +86 43185619441.
  • Yong Qiu
    Division of Spine Surgery, Department of Orthopedic Surgery, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, Jiangsu, China; Division of Spine Surgery, Department of Orthopedic Surgery, Nanjing Drum Tower Hospital, Nanjing, Jiangsu, China. Electronic address: scoliosis2002@sina.com.
  • Xin Chen
    University of Nottingham, Nottingham, United Kingdom.

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