Application of deep learning algorithm to detect and visualize vertebral fractures on plain frontal radiographs.

Journal: PloS one
Published Date: Jan 28, 2021

Abstract

BACKGROUND: Identification of vertebral fractures (VFs) is critical for effective secondary fracture prevention owing to their association with the increasing risks of future fractures. Plain abdominal frontal radiographs (PARs) are a common investigation method performed for a variety of clinical indications and provide an ideal platform for the opportunistic identification of VF. This study uses a deep convolutional neural network (DCNN) to identify the feasibility for the screening, detection, and localization of VFs using PARs.

Authors

  • Hsuan-Yu Chen
    Institute of Biomedical Engineering, National Taiwan University, Taipei City, Taiwan.
  • Benny Wei-Yun Hsu
    Department of Computer Science, National Chiao Tung University, No. 1001, Daxue Rd., East Dist., Hsinchu, 300, Taiwan, ROC.
  • Yu-Kai Yin
    Department of Computer Science, National Chiao Tung University, No. 1001, Daxue Rd., East Dist., Hsinchu, 300, Taiwan, ROC.
  • Feng-Huei Lin
    Institute of Biomedical Engineering and Nanomedicine, National Health Research Institutes, Zhunan; Institute of Biomedical Engineering, National Taiwan University.
  • Tsung-Han Yang
    Department of Orthopedics, National Taiwan University College of Medicine and National Taiwan University Hospital, Taipei, Taiwan.
  • Rong-Sen Yang
    Department of Orthopedics, National Taiwan University College of Medicine and National Taiwan University Hospital, Taipei, Taiwan.
  • Chih-Kuo Lee
    Department of Internal Medicine, National Taiwan University HsinChu Hospital, HsinChu, Taiwan.
  • Vincent S Tseng
    Computer Science and Information Engineering, National Chiao Tung University, Hsinchu, Taiwan.

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