Clinical Application of Automatic Assessment of Scoliosis Cobb Angle Based on Deep Learning.

Journal: Current medical imaging
Published Date: Jan 1, 2024

Abstract

INTRODUCTION: A recently developed deep-learning-based automatic evaluation model provides reliable and efficient Cobb angle measurements for scoliosis diagnosis. However, few studies have explored its clinical application, and external validation is lacking. Therefore, this study aimed to explore the value of automated assessment models in clinical practice by comparing deep-learning models with manual measurement methods.

Authors

  • Lixin Ni
  • Zhehao Zhang
    Department of Radiation Oncology, Washington University School of Medicine in St. Louis, St. Louis, Missouri, USA.
  • Lulin Zou
  • Jianhua Wang
    Gene Engineering Laboratory, Feed Research Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China.
  • Lijun Guo
  • Wei Qian
    Department of Electrical and Computer Engineering, University of Texas at El Paso, 500 West University Avenue, El Paso, TX 79968, USA; Sino-Dutch Biomedical and Information Engineering School, Northeastern University, No.11, Lane 3, Wenhua Road, Heping District, Shenyang, Liaoning 110819, China. Electronic address: wqian@utep.edu.
  • Lei Xu
    Key Laboratory of Biomedical Information Engineering of the Ministry of Education, Department of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China.
  • Kaiwei Xu
    China Ship Scientific Research Center, Wuxi, Jiangsu Province, China.
  • Yingqing Zeng
    Department of Radiology, The First Affiliated Hospital of Ningbo University, China.

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