Application of a deep learning algorithm for detection and visualization of hip fractures on plain pelvic radiographs.

Journal: European radiology
Published Date: Oct 1, 2019

Abstract

OBJECTIVE: To identify the feasibility of using a deep convolutional neural network (DCNN) for the detection and localization of hip fractures on plain frontal pelvic radiographs (PXRs). Hip fracture is a leading worldwide health problem for the elderly. A missed diagnosis of hip fracture on radiography leads to a dismal prognosis. The application of a DCNN to PXRs can potentially improve the accuracy and efficiency of hip fracture diagnosis.

Authors

  • Chi-Tung Cheng
    Department of Trauma and Emergency Surgery, Chang Gung Memorial Hospital, Linkou, Chang Gung University, Taoyuan, Taiwan.
  • Tsung-Ying Ho
    Departments of Nuclear Medicine and Molecular Imaging Center, Chang Gung Memorial Hospital, Linkou, Chang Gung University, Taoyuan, Taiwan.
  • Tao-Yi Lee
    Donald Bren School of Information and Computer Sciences, University of California Irvine, Irvine, CA, USA.
  • Chih-Chen Chang
    Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital, Linkou, Chang Gung University, Taoyuan, Taiwan.
  • Ching-Cheng Chou
    Department of Trauma and Emergency Surgery, Chang Gung Memorial Hospital, Linkou, Chang Gung University, Taoyuan, Taiwan.
  • Chih-Chi Chen
    Departments of Rehabilitation and physical medicine, Chang Gung Memorial Hospital, Linkou, Chang Gung University, Taoyuan, Taiwan.
  • I-Fang Chung
    Institute of Biomedical Informatics, National Yang-Ming University, Taipei, Taiwan.
  • Chien-Hung Liao
    Department of Trauma and Emergency Surgery, Chang Gung Memorial Hospital, Linkou, Chang Gung University, Taoyuan, Taiwan. surgymet@gmail.com.

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