Enhanced reliability and time efficiency of deep learning-based posterior tibial slope measurement over manual techniques.

Journal: Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA
PMID: 38796728

Abstract

PURPOSE: Multifaceted factors contribute to inferior outcomes following anterior cruciate ligament (ACL) reconstruction surgery. A particular focus is placed on the posterior tibial slope (PTS). This study introduces the integration of machine learning and artificial intelligence (AI) for efficient measurements of tibial slopes on magnetic resonance imaging images as a promising solution. This advancement aims to enhance risk stratification, diagnostic insights, intervention prognosis and surgical planning for ACL injuries.

Authors

  • Shang-Yu Yao
    Department of Orthopedic Surgery, Linkou Chang Gung Memorial Hospital, Taoyuan City, Taiwan.
  • Xue-Zhi Zhang
    Engineering Product Development, Singapore University of Technology and Design, Tampines, Singapore.
  • Soumyajit Podder
    Department of Biomedical Engineering, Chang Gung University, Taoyuan City, Taiwan.
  • Chen-Te Wu
  • Yi-Shen Chan
    Department of Orthopedic Surgery, Linkou Chang Gung Memorial Hospital, Taoyuan City, Taiwan.
  • Dan Berco
    Comprehensive Sports Medicine Center, Taoyuan Chang Gung Memorial Hospital, Taoyuan City, Taiwan.
  • Cheng-Pang Yang
    Department of Orthopedic Surgery, Linkou Chang Gung Memorial Hospital, Taoyuan City, Taiwan.

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