Accuracy of machine learning in identifying candidates for total knee arthroplasty (TKA) surgery: a systematic review and meta-analysis.

Journal: European journal of medical research
PMID: 40264241

Abstract

BACKGROUND: The application of machine learning (ML) in predicting the requirement for total knee arthroplasty (TKA) at knee osteoarthritis (KOA) patients has been acknowledged. Nonetheless, the variables employed in the development of ML models are diverse and these different approaches yield inconsistent predictive performance of models. Therefore, we conducted this systematic review and meta-analysis to explore the feasibility of ML in identifying candidates for TKA.

Authors

  • Cong Tian
    School of Computer Science and Technology, Xidian University, No. 2 South Taibai Road, Xi'an, 710071, PR China. Electronic address: ctian@mail.xidian.edu.cn.
  • Haifeng Chen
    Office of Educational Administration, Bengbu Medical College, Bengbu, China.
  • Wenhui Shao
    Department of Chinese Internal Medicine, Funan Hospital of Chinese Medicine, Fuyang, 236300, Anhui, China.
  • Ruikun Zhang
    The Third School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, 310053, Zhejiang, China.
  • Xinmiao Yao
    Department of Orthopedics, The Third Affiliated Hospital of Zhejiang Chinese Medical University (Zhongshan Hospital of Zhejiang Province), Hangzhou, 310053, Zhejiang, China. yxmzcmu@163.com.
  • Jianlong Shu
    The First School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, 310053, Zhejiang, China.

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