A development of machine learning models to preoperatively predict insufficient clinical improvement after total knee arthroplasty.

Journal: Journal of orthopaedic surgery and research
Published Date: Aug 20, 2025

Abstract

BACKGROUND: Identifying patients unlikely to achieve meaningful improvement following total knee arthroplasty (TKA) supports more effective shared decision-making (SDM). This study aimed to develop and validate machine learning (ML) models that preoperatively predict insufficient clinical improvement one year after TKA using Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) subscales and total scores, and to assess the important predictive variables.

Authors

  • Geunwu Gimm
    Department of Biomedical Engineering, Seoul National University College of Medicine, 103 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea.
  • Byoungjun Jeon
    Office of Hospital Information, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea.
  • Sung Eun Kim
    Department of Pediatrics, Incheon St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea.
  • Byeong Soo Kim
    Interdisciplinary Program in Bioengineering, Graduate School, Seoul National University, Seoul, Republic of Korea.
  • Hyuk-Soo Han
    Department of Orthopaedic Surgery, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080, South Korea.
  • Sungwan Kim
    Department of Biomedical Engineering, Seoul National University College of Medicine, Seoul, Republic of Korea.

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