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Nevertheless, the quality of evidence for the diagnosis of septic arthritis is low. In this study, the authors developed a machine learning\u2010based diagnostic algorithm for septic arthritis of the native knee using clinical data in an emergency department and validated its diagnostic accuracy.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Patients (<jats:italic>n<\/jats:italic>\u2009=\u2009326) 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\u2010operating 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\u2010learning 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\u2010learning model and each variable were compared.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Synovial white blood cell (WBC) count was significantly higher in septic arthritis than in inflammatory arthritis in the multivariate analysis (<jats:italic>P<\/jats:italic>\u2009=\u20090.001). In the ROC comparison analysis, synovial WBC count yielded a significantly higher AUC than all other single variables (<jats:italic>P<\/jats:italic>\u2009=\u20090.002). The diagnostic model using the XGBoost algorithm yielded a higher AUC (0.831, 95% confidence interval 0.751\u20130.923) than synovial WBC count (0.740, 95% confidence interval 0.684\u20130.791; <jats:italic>P<\/jats:italic>\u2009=\u20090.033). The developed algorithm was deployed as a free access web\u2010based application (<jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"http:\/\/www.septicknee.com\">www.septicknee.com<\/jats:ext-link>).<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>The diagnosis of septic arthritis of the knee might be improved using a machine learning\u2010based prediction model.<\/jats:p><\/jats:sec><jats:sec><jats:title>Level of evidence<\/jats:title><jats:p>Diagnostic study Level III (Case\u2013control study).<\/jats:p><\/jats:sec>","DOI":"10.1007\/s00167-020-06418-2","type":"journal-article","created":{"date-parts":[[2021,1,15]],"date-time":"2021-01-15T15:44:02Z","timestamp":1610725442000},"page":"3142-3148","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Machine\u2010learning algorithm that can improve the diagnostic accuracy of septic arthritis of the knee"],"prefix":"10.1002","volume":"29","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2874-6675","authenticated-orcid":false,"given":"Eun\u2010Seok","family":"Choi","sequence":"first","affiliation":[{"name":"<!--<label>1<\/label>--> Department of Orthopaedic Surgery Chungnam National University School of Medicine, Chungnam National University Hospital 266 Munhwa\u2010ro, Jung\u2010gu 35015 Daejeon Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jae Ang","family":"Sim","sequence":"additional","affiliation":[{"name":"<!--<label>2<\/label>--> Department of Orthopaedic Surgery Gachon University College of Medicine, Gil Medical Centre Incheon Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Young Gon","family":"Na","sequence":"additional","affiliation":[{"name":"<!--<label>3<\/label>--> Department of Orthopaedic Surgery CM Hospital Seoul Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jong\u2010 Keun","family":"Seon","sequence":"additional","affiliation":[{"name":"<!--<label>4<\/label>--> Department of Orthopaedic Surgery Chonnam National University School of Medicine, Chonnam National University Hospital Gwangju Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hyun Dae","family":"Shin","sequence":"additional","affiliation":[{"name":"<!--<label>1<\/label>--> Department of Orthopaedic Surgery Chungnam National University School of Medicine, Chungnam National University Hospital 266 Munhwa\u2010ro, Jung\u2010gu 35015 Daejeon Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2021,1,15]]},"reference":[{"key":"e_1_2_11_2_2","doi-asserted-by":"publisher","DOI":"10.1136\/jcp.2006.043950"},{"key":"e_1_2_11_3_2","doi-asserted-by":"publisher","DOI":"10.1111\/j.1553\u20102712.2011.01121.x"},{"key":"e_1_2_11_4_2","doi-asserted-by":"publisher","DOI":"10.1017\/cem.2014.56"},{"key":"e_1_2_11_5_2","doi-asserted-by":"publisher","DOI":"10.2307\/2531595"},{"key":"e_1_2_11_6_2","doi-asserted-by":"publisher","DOI":"10.2106\/JBJS.RVW.19.00059"},{"key":"e_1_2_11_7_2","first-page":"4058403","article-title":"SGB\u2010ELM: an advanced stochastic gradient boosting\u2010based ensemble scheme for extreme learning machine","volume":"2018","author":"Guo H","year":"2018","journal-title":"ComputIntell Neurosci"},{"key":"e_1_2_11_8_2","doi-asserted-by":"publisher","DOI":"10.1093\/rheumatology\/40.1.24"},{"key":"e_1_2_11_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jemermed.2010.05.029"},{"key":"e_1_2_11_10_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00167\u2010019\u201005602\u20103"},{"key":"e_1_2_11_11_2","unstructured":"KoS JoC ChangCB LeeYS MoonYW YoumJWet al. 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