{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T04:03:29Z","timestamp":1790741009781,"version":"4.1.0"},"reference-count":35,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2024,11,22]],"date-time":"2024-11-22T00:00:00Z","timestamp":1732233600000},"content-version":"vor","delay-in-days":52,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["esskajournals.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J. exp. orthop."],"published-print":{"date-parts":[[2024,10]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Purpose<\/jats:title><jats:p>This study evaluated the performance of three machine learning (ML) algorithms\u2014decision tree (DT), multilayer perceptron (MLP) and extreme gradient boosting (XGB)\u2014in identifying regular athletes who suffered a knee injury several months to years prior. In addition, the contribution of psychological variables in addition to biomechanical ones in the classification performance of the ML algorithms was assessed, to better identify factors to get back to competitive sport with the lowest possible risk of new knee injury.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>A cohort of 96 athletes, 36 with prior knee injuries, practicing an average of 5.7\u2009\u00b1\u20092.4\u2009h per week, participated in a horizontal force\u2010velocity test on a ballistic ergometer providing data of force, velocity and power from each lower limb. They also completed a psychological questionnaire, which included components from the Knee Injury and Osteoarthritis Outcome Score (KOOS) and the Sport Anxiety Scale (SAS). The three ML algorithms were trained on a thousand different train\u2010test sets. Also, Shapley values were calculated for each input variable of a data set to highlight its contribution to the prediction from an ML model.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Over a thousand cross\u2010validations, higher area under the curve (AUC) values were obtained when accounted for the psychological attributes (<jats:italic>p<\/jats:italic>\u2009&lt;\u20090.001). Also, higher AUC values were obtained from MLP compared to XGB or DT (<jats:italic>p<\/jats:italic>\u2009&lt;\u20090.001). XGB exhibited higher AUC values than DT (<jats:italic>p<\/jats:italic>\u2009&lt;\u20090.001).<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>Our results suggested that psychological factors play a more important role in recognition than biomechanical factors, with KOOS and SAS scores ranking high in the list of influential factors. Additionally, the computing stability of MLP could be recommended for classification tasks in the context of knee injuries.<\/jats:p><\/jats:sec><jats:sec><jats:title>Level of Evidence<\/jats:title><jats:p>Level III.<\/jats:p><\/jats:sec>","DOI":"10.1002\/jeo2.70081","type":"journal-article","created":{"date-parts":[[2024,11,22]],"date-time":"2024-11-22T05:55:02Z","timestamp":1732254902000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Machine learning in knee injury sequelae detection: Unravelling the role of psychological factors and preventing long\u2010term sequelae"],"prefix":"10.1002","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-9528-4200","authenticated-orcid":false,"given":"Cl\u00e9ment","family":"LIPPS LENE","sequence":"first","affiliation":[{"name":"Universit\u00e9 de Bordeaux, Laboratoire IMS, UMR 5218, PMH_DySCo Pessac France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4778-4514","authenticated-orcid":false,"given":"Julien","family":"Frere","sequence":"additional","affiliation":[{"name":"Univ. Grenoble Alpes, CNRS, Grenoble INP, GIPSA\u2010Lab Grenoble France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3281-504X","authenticated-orcid":false,"given":"Thierry","family":"Weissland","sequence":"additional","affiliation":[{"name":"Universit\u00e9 de Bordeaux, Laboratoire IMS, UMR 5218, PMH_DySCo Pessac France"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2024,11,22]]},"reference":[{"key":"e_1_2_12_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2021.10.035"},{"key":"e_1_2_12_3_1","doi-asserted-by":"publisher","DOI":"10.1590\/1413-785220202806236114"},{"key":"e_1_2_12_4_1","doi-asserted-by":"publisher","DOI":"10.1136\/bjsm.2010.076364"},{"key":"e_1_2_12_5_1","doi-asserted-by":"publisher","DOI":"10.1177\/0363546512472330"},{"key":"e_1_2_12_6_1","doi-asserted-by":"crossref","unstructured":"Chen T.&Guestrin C.(2016) XGBoost: a scalable tree boosting system. 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