Machine Learning-Based CT Radiomics Model to Predict the Risk of Hip Fragility Fracture.

Journal: Academic radiology
PMID: 39904664

Abstract

RATIONALE AND OBJECTIVES: This research aimed to develop a combined model based on proximal femur attenuation values and radiomics features at routine CT to predict hip fragility fracture using machine learning methods.

Authors

  • Jinglei Yuan
    Department of Medical Imaging, the First Affiliated Hospital of Guangdong Pharmaceutical University, Guangzhou 510080, China (J.Y., B.L., J.W., L.M.).
  • Bing Li
  • Chu Zhang
    School of Information Engineering, Huzhou University, Huzhou 313000, China.
  • Jing Wang
    Endoscopy Center, Peking University Cancer Hospital and Institute, Beijing, China.
  • Bingsheng Huang
    School of Biomedical Engineering, Shenzhen University Health Sciences Center, Shenzhen, Guangdong 518060, P.R.China.
  • Liheng Ma
    Department of Medical Imaging, the First Affiliated Hospital of Guangdong Pharmaceutical University, Guangzhou 510080, China (J.Y., B.L., J.W., L.M.). Electronic address: liheng.ma@163.com.

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