A development of machine learning models to preoperatively predict insufficient clinical improvement after total knee arthroplasty
The ML models demonstrated good performance in preoperatively predicting insufficient clinical improvement at 1 year after TKA based on WOMAC. These models have the potential to enhance SDM and perioperative patient management by preoperatively identifying approximately 70% to over 80% of patients likely to experience insufficient clinical improvement, with a specificity of about 80%, and by providing explanations regarding associated factors.
Authors
- Geunwu Gimm (ORCID: https://orcid.org/0000-0002-7414-4125)
- Byoungjun Jeon (ORCID: https://orcid.org/0000-0003-0127-6490)
- Hyuk‐Soo Han (ORCID: https://orcid.org/0000-0003-1229-8863)
- Sung Eun Kim (ORCID: https://orcid.org/0000-0002-1954-9875)
- Sungwan Kim (ORCID: https://orcid.org/0000-0002-9318-849X)
- Byeong Soo Kim (ORCID: https://orcid.org/0000-0001-8767-9842)
Institutions
- Seoul National University (KR)
- New Generation University College (ET)
- Seoul National University Hospital (KR)
- National University College (PR)
Publication Details
- Journal
- Journal of Orthopaedic Surgery and Research
- Published
- 2025-08-20
- DOI
- https://doi.org/10.1186/s13018-025-06206-z
- Primary Topic
- Total Knee Arthroplasty Outcomes
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Korea Health Industry Development Institute