Machine learning based prediction of popliteal crease obliquity angle from step-down kinematics and its application in early osteoarthritis classification.

Objective: 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. Methods: This cross-sectional study employed a two-phase machine learning approach in 43 manufacturing workers (86 legs, aged 40-70 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 Results: Extreme gradient boosting achieved optimal Phase I performance in predicting PCOA from step-down kinematics (R Conclusions: This study establishes a strong functional-anatomical relationship between step-down kinematics and PCOA, demonstrating PCOA's association with EOA status. The 7° threshold, measurable via smartphone photography, offers a practical alternative to complex kinematic analysis for early OA screening in clinical practice.