Predicting periprosthetic joint infection in primary total knee arthroplasty: a machine learning model integrating preoperative and perioperative risk factors
Our study presented the first machine learning model in Asia to predict periprosthetic joint infection following primary total knee arthroplasty. We enhanced the model's usability by providing global and local interpretations. This tool provides preoperative and perioperative risk assessment for periprosthetic joint infection and opens the potential for better individualized optimization before total knee arthroplasty.
Authors
- Pky Chiu (ORCID: https://orcid.org/0000-0001-9284-5817)
- PK Chan (ORCID: https://orcid.org/0000-0002-1969-0895)
- Chunyi Wen (ORCID: https://orcid.org/0000-0003-1949-7822)
- Lawrence Chun‐Man Lau (ORCID: https://orcid.org/0000-0002-2245-9627)
- Tianshu Jiang (ORCID: https://orcid.org/0000-0003-1128-0450)
- Henry Fu (ORCID: https://orcid.org/0000-0001-7520-6845)
- Ka Chun Thomas Leung
- Michelle Hilda Luk (ORCID: https://orcid.org/0000-0001-6859-9011)
- Jiang Zhang (ORCID: https://orcid.org/0000-0001-5807-1686)
- Amy Cheung (ORCID: https://orcid.org/0000-0002-4992-6176)
- Man Hong Cheung (ORCID: https://orcid.org/0000-0001-6617-8088)
- Yuk Yee Chong
Institutions
- Hong Kong Polytechnic University (HK)
- Chinese University of Hong Kong (HK)
- Queen Mary Hospital (CN)
- University of Hong Kong (HK)
Publication Details
- Journal
- BMC Musculoskeletal Disorders
- Published
- 2025-03-11
- DOI
- https://doi.org/10.1186/s12891-025-08296-6
- Citations
- 5
- Primary Topic
- Orthopedic Infections and Treatments
- Type
- article
- Field-Weighted Citation Impact
- 10.03