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 (R2) and area under the curve (AUC). To interpret and explain the predictions, we used SHapley additive explanation values and partial dependence analysis.

RESULTS

Extreme gradient boosting achieved optimal Phase I performance in predicting PCOA from step-down kinematics (R2=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° threshold above which EOA probability increased.

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.