Malalignment Detection in TKA: A Digital Twin Framework Using Instrumented Tibial Trays

Component alignment in total knee arthroplasty (TKA) is critical for postoperative success, as malalignment can lead to aseptic loosening. This study explores the potential for detecting knee prosthesis malalignment through strain analysis of the tibial tray membrane, leveraging a digital twin approach, which combines physics-based simulation with data-driven modeling. A finite element model (FEM) of the complete femur–insert–tibia system, equipped with a strain-based sensing tray, was used to analyze the mechanical response of the assembly under various loading conditions. Malalignment was simulated by varying the angle between the femur and insert in the coronal plane. The metric of interest is strain at the surface of the membrane. The resulting strain values were converted into synthetic voltage signals, representing the expected measurement of the real strain gauge output. These signals were used to train a neural network model to predict the prosthesis malalignment angle. The model demonstrated a strong correlation between malalignment and strain signal with a root mean square error (RMSE) of 0.02° on the simulated data. These results support the creation of a virtual alignment sensor, enhancing the capabilities of smart orthopedic implants for intraoperative and postoperative assessment.