A multiview deep learning-based prediction pipeline augmented with confident learning can improve performance in determining knee arthroplasty candidates.

Journal: Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA
Published Date: Aug 1, 2024

Abstract

PURPOSE: Preoperative prudent patient selection plays a crucial role in knee osteoarthritis management but faces challenges in appropriate referrals such as total knee arthroplasty (TKA), unicompartmental knee arthroplasty (UKA) and nonoperative intervention. Deep learning (DL) techniques can build prediction models for treatment decision-making. The aim is to develop and evaluate a knee arthroplasty prediction pipeline using three-view X-rays to determine the suitable candidates for TKA, UKA or are not arthroplasty candidates.

Authors

  • Hongzhi Liu
    Tuberculosis Department, Shanxi Linfen Third People's Hospital, Linfen City, Shanxi Province 041000, China.
  • Xiaoyao Wang
    Department of Industrial & Manufacturing Systems Engineering, School of Mechanical Engineering & Automation, Beihang University, Beijing, China.
  • Xinqiu Song
    Department of General Surgery, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
  • Bing Han
    Harbin University of Commerce, Harbin, China.
  • Chuiqing Li
    Department of Orthopaedics, Weifang Hospital of Traditional Chinese Medicine, Weifang, Shandong, China.
  • Fuzhou Du
    Department of Industrial & Manufacturing Systems Engineering, School of Mechanical Engineering & Automation, Beihang University, Beijing, China.
  • Hongmei Zhang
    School of Mechanical and Electrical Engineering, Henan Agricultural University, Zhengzhou, Henan, China.

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