{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T12:26:18Z","timestamp":1777551978778,"version":"3.51.4"},"reference-count":39,"publisher":"SAGE Publications","license":[{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["DIGITAL HEALTH"],"published-print":{"date-parts":[[2026,2]]},"abstract":"<jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>Early osteoarthritis (EOA) detection is crucial for timely intervention, yet current methods often fail to identify early-stage disease. The popliteal crease obliquity angle (PCOA) shows promise as a novel anatomical marker, but its relationship with functional movement patterns remains unclear.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      This cross-sectional study employed a two-phase machine learning approach in 43 manufacturing workers (86 legs, aged 40\u201370 years). Phase I used regression analysis to predict PCOA from step-down frontal plane kinematics using five algorithms. Phase II applied PCOA measurements to classify EOA status based on the Early Osteoarthritis Questionnaire. Performance was evaluated using coefficient of determination (R\n                      <jats:sup>2<\/jats:sup>\n                      ) and area under the curve (AUC). To interpret and explain the predictions, we used SHapley additive explanation values and partial dependence analysis.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Extreme gradient boosting achieved optimal Phase I performance in predicting PCOA from step-down kinematics (R\n                      <jats:sup>2<\/jats:sup>\n                      =0.668) on the hold-out test set. Femur horizontal displacement (valgus movement), pelvis horizontal displacement, and ankle horizontal displacement were key predictors. Random Forest demonstrated superior Phase II performance (AUC=0.885, accuracy=0.824) on the hold-out test set, with PCOA as the dominant feature. Partial dependence analysis identified a critical 7\u00b0 threshold above which EOA probability increased.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>This study establishes a strong functional-anatomical relationship between step-down kinematics and PCOA, demonstrating PCOA's association with EOA status. The 7\u00b0 threshold, measurable via smartphone photography, offers a practical alternative to complex kinematic analysis for early OA screening in clinical practice.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/20552076261435079","type":"journal-article","created":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T07:50:27Z","timestamp":1774425027000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Machine learning based prediction of popliteal crease obliquity angle from step-down kinematics and its application in early osteoarthritis classification"],"prefix":"10.1177","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2050-5503","authenticated-orcid":false,"given":"Ui-Jae","family":"Hwang","sequence":"first","affiliation":[{"name":"The Hong Kong Polytechnic 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University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2859-6486","authenticated-orcid":false,"given":"Il-Kyu","family":"Ahn","sequence":"additional","affiliation":[{"name":"Yonsei University, Laboratory of Kinetic Ergocise Based on Movement 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