Automated opportunistic screening for osteoporosis using deep learning-based automatic segmentation and radiomics on proximal femur images from low-dose abdominal CT.

Journal: BMC musculoskeletal disorders
PMID: 40241032

Abstract

RATIONALE AND OBJECTIVES: To establish an automated osteoporosis detection model based on low-dose abdominal CT (LDCT). This model combined a deep learning-based automatic segmentation of the proximal femur with a radiomics-based bone status classification.

Authors

  • Changyu Du
    Department of Radiology, First Affiliated Hospital of Dalian Medical University, Xigang District, Lianhe Road, No.193, Dalian, China.
  • Jian He
    School of Software Engineering, Beijing University of Technology, Beijing, China. Electronic address: jianhee@bjut.edu.cn.
  • Qiye Cheng
    Department of Radiology, First Affiliated Hospital of Dalian Medical University, Xigang District, Lianhe Road, No.193, Dalian, China.
  • Mengting Hu
    School of Civil Engineering and Architecture, Xiamen University of Technology, Xiamen 361024, China.
  • Jingyi Zhang
    Department of Health Management of Public Health, College of Public Health, Zhengzhou University, 100 Kexue Road, Gaoxin district, Zhengzhou, 450001, Henan, China.
  • Jiageng Shen
    Department of Radiology, First Affiliated Hospital of Dalian Medical University, Xigang District, Lianhe Road, No.193, Dalian, China.
  • Shigeng Wang
    Department of Radiology, First Affiliated Hospital of Dalian Medical University, Shahekou District, Lianhe Road, Dalian, Liaoning, China (S.W., X.T., Y.F., M.H., Y.L., X.F.).
  • Yijun Liu
  • Jianying Li
    CT Research Center, GE Healthcare China, Beijing 100176, China.
  • Wei Wei
    Dept. Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA.

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