Combining Deep Learning and Radiomics for Automated, Objective, Comprehensive Bone Mineral Density Assessment From Low-Dose Chest Computed Tomography.

Journal: Academic radiology
PMID: 37730494

Abstract

RATIONALE AND OBJECTIVES: To develop an intelligent diagnostic model for osteoporosis screening based on low-dose chest computed tomography (LDCT). The model incorporates automatic deep-learning thoracic vertebrae of cancellous bone (TVCB) segmentation model and radiomics analysis.

Authors

  • 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.).
  • Xiaoyu Tong
    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.).
  • Yong Fan
    CPB/ECMO Children's Hospital, Zhejiang University School of Medicine, 310052 Hangzhou, Zhejiang, China.
  • Mengting Hu
    School of Civil Engineering and Architecture, Xiamen University of Technology, Xiamen 361024, China.
  • Jingjing Cui
    Jiangxi Key Laboratory of Bioprocess Engineering, Jiangxi Science and Technology Normal University, Nanchang, 330013 People's Republic of China.
  • Jianying Li
    CT Research Center, GE Healthcare China, Beijing 100176, China.
  • Yijun Liu
  • Qingzhu Xiao
    Dongbei University of Finance and Economics, Dalian, China (Q.X.).
  • Xin Fang
    School of Information Science and Technology, University of Science and Technology of China, Hefei 230022, China.

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