Brandon D. Hines

Smart knee replacement technology seeks to provide an in vivo method of tracking long-term postoperative joint loads with the goal of identifying clinically relevant phenomena linked to postoperative dissatisfaction in real time. This study evaluated the ability of a piezoelectric compartmental force and compartmental center of pressure sensing total knee replacement to sense condylar lift-off, which is a clinically relevant phenomenon commonly attributed to postoperative dissatisfaction. A commercially available total knee replacement was modified to include six piezoelectric transducers capable of measuring compartmental forces and tibiofemoral centers of pressure on the articular surface of the tibial bearing insert. The smart knee replacement was evaluated with a six-degree-of-freedom joint motion simulator applying a varus lift-off profile. The study demonstrated that the lift-off was evident in both the sensed joint loads and the localized tibiofemoral centers of pressure obtained from the piezoelectric sensing system. The results indicated that the piezoelectric smart knee replacement could be effective for detecting this clinically problematic mechanical issue.

Machine learning (ML) is capable of aiding and improving medical diagnostics for a wide variety of pathologies. When used to process data from smart implantable medical devices, ML can offer timely and automated diagnostic tools to improve patient care. However, diagnostic ML models that influence healthcare decisions must have outputs that can be understood and trusted. Currently, ML-based medical diagnostic research focuses mainly on accuracy, lacking investigation of interpretability, explainability, and trust in the models — fundamental principles of diagnostic ML. To address this gap, this study seeks to improve explainability in ML models trained to diagnose aseptic tibial loosening in smart piezoelectric total knee replacements (TKRs). Specifically, a pathway to explainability is presented by applying the local interpretable model-agnostic explanations (LIME) method to interpret previously trained [ ]-nearest neighbor (KNN), support vector machine (SVM), and discriminant analysis (DA) ML models that classify cement damage from electromechanical impedance (EMI) signatures of piezoelectric-instrumented simulated TKRs. Two simple yet novel feature engineering techniques are proposed to align the models with domain knowledge of piezoelectric impedance-based structural health monitoring (SHM), thus improving explainability. These feature engineering techniques are broadly applicable to data types where adjacent features have inherent relations (i.e., time series, spectra, images, etc.). The original KNN, SVM, and DA models demonstrated explainability scores of 52%, 64.4%, and 42.1%, respectively. The first feature engineering technique improved the KNN and SVM scores to 84.6% and 87.6%, respectively, with DA falling to 29.5%. The second feature engineering technique improved the KNN and SVM scores to 74.5% and 81.1%, respectively, with DA falling to 33.5%. The pathway to explainability and the feature engineering techniques presented in this study yield models with improved explainability that are better aligned with domain knowledge, while either improving (SVM) or maintaining (KNN) their original accuracy.