Performance evaluation of a deep learning-based cascaded HRNet model for automatic measurement of X-ray imaging parameters of lumbar sagittal curvature.

Journal: European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society
PMID: 37787781

Abstract

PURPOSE: To develop a deep learning-based cascaded HRNet model, in order to automatically measure X-ray imaging parameters of lumbar sagittal curvature and to evaluate its prediction performance.

Authors

  • Yuhua Wu
    The First Clinical Medical College of Gansu University of Chinese Medicine, Lanzhou, 730000, Gansu, China.
  • Xiaofei Chen
    Oncology Biometrics, AstraZeneca, Gaithersburg, Maryland, USA.
  • Fuwen Dong
    Department of Radiology, Gansu Provincial Hospital of Traditional Chinese Medicine (The first affiliated hospital of Gansu University of Traditional Chinese Medicine), Lanzhou, 730050, Gansu, China.
  • Linyang He
    Hangzhou Jianpei Technology Co., Ltd, Hangzhou, China.
  • Guohua Cheng
    Institute of Science and Technology for Brain-Inspired Intelligence, Ministry of Education-Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Shanghai, 200433, China.
  • Yuwen Zheng
    The First Clinical Medical College of Gansu University of Chinese Medicine, Lanzhou, 730000, Gansu, China.
  • Chunyu Ma
    Gansu University of Traditional Chinese Medicine, Lanzhou, Gansu 730000, China.
  • Hongyan Yao
    Gansu University of Traditional Chinese Medicine, Lanzhou, Gansu 730000, China.
  • Sheng Zhou
    Department of The First Clinical Medical College of Gansu, University of Chinese Medicine, Lanzhou, Gansu, China.

Keywords

External Resources

Popular Topics
Recent Journals