Abstract

Knee osteoarthritis (OA) is the most common musculoskeletal disorder affecting all populations. One common knee OA symptom is instability; thus its assessment could allow diagnosing and following-up of the disease without using conventional imaging techniques, such as plain radiography or magnetic resonance imaging (MRI). Knee kinematic measurements using accelerometers could provide a low-cost and non-invasive option to quantify knee instability. The aim of this study was to assess the relationships between kinematic data, instability parameters derived from the imaging techniques, goniometer-based measurements, and radiological OA stage. The right knees of 66 females (44–67 years) were examined using MRI, plain radiography, and goniometer-based angle measurement. Kellgren–Lawrence (KL) grade and the joint line convergence angle (JLCA) were determined from the radiographs. Cartilage thickness and OA score (MOAKS) were derived from the MRI. A ratio between lateral and medial cartilage thicknesses was calculated from the average thickness of segmented cartilage over the weight bearing area (MRI_ratio). Accelerometers attached to thigh and shank were used to record kinematic signals during a one-leg-stand test. Power of the accelerometer signals along the anatomical longitudinal axis (P_acc) was used as a measure of knee instability. Finally, Spearman’s correlations between the acquired parameters and KL grade/MOAKS scores were calculated. Leave-one-out cross-validation and logistic regression were used to discriminate OA subjects (KL ≥ 2). All the instability parameters (P_acc, JLCA and MRI_ratio), except the goniometer angle, showed significant correlations with KL grading (rho = 0.32–0.64, p < 0.01) and MOAKS composite score (rho = 0.35–0.56, p < 0.01). Both P_acc and JLCA showed higher areas under the ROC curve to discriminate OA (AUC = 0.76 and AUC = 0.78) than MRIratio and goniometer angle (AUC = 0.55 and AUC = 0.56). Our results demonstrate the clinical potential of kinematic knee instability measurements using low-cost accelerometers. Such approach could become a potential new tool in OA diagnostics.