Deep learning for automated measurement of CSA related acromion morphological parameters on anteroposterior radiographs.

Journal: European journal of radiology
PMID: 37714046

Abstract

BACKGROUND: The Critical Shoulder Angle Related Acromion Morphological Parameter (CSA- RAMP) is a valuable tool in the analyzing the etiology of the rotator cuff tears (RCTs). However, its clinical application has been limited by the time-consuming and prone to inter- and intra-user variability of the measurement process.

Authors

  • Yamuhanmode Alike
    Department of Orthopaedic Surgery, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China.
  • Cheng Li
    College of Food and Bioengineering, Henan University of Science and Technology, Luoyang, China.
  • Jingyi Hou
    Department of Orthopaedic Surgery, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China.
  • Yi Long
    State Key Laboratory of Robotics and System, Harbin Institute of Technology (HIT), Harbin 150001, China. scdxhgd@gmail.com.
  • Zongda Zhang
    Department of Orthopaedic Surgery, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China.
  • Mengjie Ye
    Intelligent Engineering and Education Application Research Center, Zhuhai Campus of Beijing Normal University, Zhuhai, China. Electronic address: mjye@bnu.edu.cn.
  • Rui Yang
    Department of Biomedical Informatics, Yong Loo Lin School of Medicine National University of Singapore Singapore Singapore.

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