Classifying Pre-Radiographic Osteoarthritis of the Knee Using Wearable Acoustics Sensing at the Point of Care

This study was undertaken to determine if knee acoustic emissions (KAEs) measured at the point of care with a wearable device can classify knees with pre-radiographic osteoarthritis (pre-OA) from healthy knees. We performed a single-center cross-sectional observational study comparing KAEs in healthy knees to knees with clinical symptoms compatible with knee OA that did not meet classification criteria for radiographic knee OA. KAEs were measured during scripted maneuvers performed in clinic exam rooms or similarly noisy medical center locations in healthy (
${n}$
= 20), pre-OA (
${n}$
= 11), and, for comparison, OA (
${n}$
= 12) knees. Acoustic features were extracted from the KAEs and used to train models to classify pre-OA, OA, and control knees with logistic regression. Model performance was measured and optimized with leave-one-out cross-validation. Regressive sensitivity analysis was performed to combine acoustic information from individual maneuvers to further optimize performance. Test–retest reliability of KAEs was measured with intraclass correlation analysis. Classification models trained with KAEs were accurate for both pre-OA and OA (94% accurate, 0.96 and 0.99 area under a receiver operating characteristic curve (AUC), respectively). Acoustic features selected for use in the optimized models had high test–retest reliability by intrasession and intersession intraclass correlation analysis (mean intraclass correlation coefficient 0.971 ± 0.08 standard deviation). Analysis of KAEs measured in acoustically uncontrolled medical settings using an easily accessible wearable device accurately classified pre-OA knees from healthy control knees in our small cohort. Accessible methods of identifying pre-OA could enable regular joint health monitoring and improve OA treatment and rehabilitation outcomes.