Validating Joint Acoustic Emissions Models as a Generalizable Predictor of Joint Health

Joint acoustic emissions (JAEs) have been used as a non-invasive sensing modality of joint health for different conditions such as acute injuries, osteoarthritis (OA), and rheumatoid arthritis (RA). Recent hardware improvements for sensing JAEs have made at-home sensing to supplement clinical visits a possibility. To complement these advances, models must be improved for JAEs to function as generalizable predictors of joint health. Addressing this need, this work investigates the effects of recording setup, location-specific factors, and participant population on previously validated JAE models. The effect of recording setup is first investigated by testing a model developed previously for a wearable brace to predict erythrocyte sedimentation rate (ESR) in participants with RA on benchtop data, resulting in an area under the receiver-operating characteristic curve (AUC), sensitivity, and specificity of 0.79, 0.73, and 0.81 respectively. Investigating the effects of participant population type and location-specific factors, a feature-based model and a convolutional neural network (CNN) were both trained with healthy and RA data to predict ESR level, and then tested on a new dataset containing healthy, pre-radiographic osteoarthritis (Pre-OA), and OA data. The feature-based model had an AUC of 0.69 and 0.94, a sensitivity of 0.38 and 0.80, and a sensitivity of 1, while the CNN had an AUC of 0.85 and 0.99, a sensitivity of 0.50 and 1, and a specificity of 0.90 for detecting Pre-OA and OA respectively. The ability to generalize models across setup, location, and participant population provides a foundation for using JAEs as a measure of joint health.