ArticleKnee surgery, sports traumatology, arthroscopy : official journal of the ESSKA2021
Machine-learning algorithm that can improve the diagnostic accuracy of septic arthritis of the knee.
Article in Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 13 citations in OpenAlex.
- A comparative analysis of sagittal, coronal, and axial magnetic resonance imaging planes in diagnosing anterior cruciate ligament and meniscal tears via a deep learning model: emphasizing the unexpected importance of the axial plane.Quantitative imaging in medicine and surgery · 2025Article
- Comparative Outcomes of Arthroscopic versus Open Surgical Approaches in the Management of Septic Arthritis of the Knee: A Retrospective Study.Journal of orthopaedic case reports · 2025Article
- Septic Arthritis Modeling Using Sonographic Fusion with Attention and Selective Transformation: a Preliminary Study.Journal of imaging informatics in medicine · 2025Article
- Exosome-Machine Learning Integration in Biomedicine: Advancing Diagnosis and Biomarker Discovery.Current medicinal chemistry · 2025Review
- Exploring the impact of pathogenic microbiome in orthopedic diseases: machine learning and deep learning approaches.Frontiers in cellular and infection microbiology · 2024Review
- Violation of expectations is correlated with satisfaction following hip arthroscopy.Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA · 2023Article
- Diagnosis of septic arthritis in the acute care setting: the value of routine intra-operative sample culture.Rheumatology advances in practice · 2023Article
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Authors and funding
5 authors at 4 institutions in 1 country.
Funding
Abstract
purposePrompt diagnosis and treatment of septic arthritis of the knee is crucial. Nevertheless, the quality of evidence for the diagnosis of septic arthritis is low. In this study, the authors developed a machine learning-based diagnostic algorithm for septic arthritis of the native knee using clinical data in an emergency department and validated its diagnostic accuracy.
methodsPatients (n = 326) who underwent synovial fluid analysis at the emergency department for suspected septic arthritis of the knee were enrolled. Septic arthritis was diagnosed in 164 of the patients (50.3%) using modified Newman criteria. Clinical characteristics of septic and inflammatory arthritis were compared. Area under the receiver-operating characteristic (ROC) curve (AUC) statistics was applied to evaluate the efficacy of each variable for the diagnosis of septic arthritis. The dataset was divided into independent training and test sets (comprising 80% and 20%, respectively, of the data). Supervised machine-learning techniques (random forest and eXtreme Gradient Boosting: XGBoost) were applied to develop a diagnostic model using the training dataset. The test dataset was subsequently used to validate the developed model. The ROC curves of the machine-learning model and each variable were compared.
resultsSynovial white blood cell (WBC) count was significantly higher in septic arthritis than in inflammatory arthritis in the multivariate analysis (P = 0.001). In the ROC comparison analysis, synovial WBC count yielded a significantly higher AUC than all other single variables (P = 0.002). The diagnostic model using the XGBoost algorithm yielded a higher AUC (0.831, 95% confidence interval 0.751-0.923) than synovial WBC count (0.740, 95% confidence interval 0.684-0.791; P = 0.033). The developed algorithm was deployed as a free access web-based application ( www.septicknee.com ).
conclusionThe diagnosis of septic arthritis of the knee might be improved using a machine learning-based prediction model. LEVEL OF EVIDENCE: Diagnostic study Level III (Case-control study).
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