Effect of carbon-fiber-reinforced polyetheretherketone on stress distribution in a redesigned tumor-type knee prosthesis: a finite element analysis

Background: Surgery for bone tumors around the knee often involves extensive resection, making the subsequent prosthetic reconstruction challenging. While carbon fiber-reinforced polyetheretherketone (CF-PEEK) has been widely used in orthopedic implants, its application in tumor-type prosthesis is limited. This study aims to evaluate the feasibility of using 30wt% and 60wt% carbon fiber-reinforced polyetheretherketone (CF30-PEEK and CF60-PEEK) as materials for a redesigned tumor-type knee prosthesis through numerical analysis. Methods: A knee joint model based on CT data was created, and the resection and prosthetic reconstruction were simulated. Three finite element models of the prostheses, representing the initial and updated designs with CoCrMo and CFR-PEEK components, were constructed. Loading conditions during standing and squatting were simulated with forces of 700 N and 2800 N, respectively. Finite element analysis was used to analyze the von Mises stress and stability of all components for each prosthesis type. Results: After improvements in both material and design, the new Type 3 prosthesis showed significantly lower overall stress with stress being evenly distributed. Compared with the initial design, the maximum von Mises stress in Type 3 was reduced by 53.9% during standing and 74.2% during squatting. In the standing position, the maximum stress in the CF30-PEEK femoral component decreased by 57.3% compared with the initial design which was composed of CoCrMo, while the stress in the CF60-PEEK cardan shaft remained consistent. In the squatting position, the maximum stress in the femoral component decreased by 81.9%, and the stress in the cardan shaft decreased by 46.5%. Conclusion: The incorporation of CF30-PEEK effectively transmits forces and reduces stress concentration on the femoral component, while CF60-PEEK in the redesigned cardan shaft significantly reduces stress while maintaining stiffness. The redesigned prosthesis effectively conducts loading force and demonstrates favorable biomechanical characteristics, indicating the promising potential of utilizing CF30-PEEK and CF60-PEEK materials for tumor-type knee prostheses. The findings of this study could provide novel insights for the design and development of tumor-type knee prostheses.

A CONSISTENT DATA FUSION APPROACH FOR UNCERTAINTY QUANTIFICATION IN RIGID MUSCULOSKELETAL SIMULATION

Uncertainty quantification in rigid musculoskeletal modeling is essential to analyze the risks related to the simulation outcomes. Data fusion from multiple sources is a potential solution to reduce data uncertainties. This present study aimed at proposing a new data fusion rule leading to a more consistent and coherent data for uncertainty quantification. Moreover, a new uncertainty representation was developed using imprecise probability approach. A biggest maximal coherent subsets (BMCS) operator was defined to fuse interval-valued data ranges from multiple sources. Fusion-based probability-box structure was developed to represent the data uncertainty. Case studies were performed for uncertainty propagation through inverse dynamics and static optimization algorithms. Hip joint moment and muscle force estimation were computed under effect of the uncertainties of thigh mass and muscle properties. Respective p-boxes of these properties were generated. Regarding the uncertainty propagation analysis, correlation coefficients showed a very good value ([Formula: see text]) for the proposed fusion operator according to classical operators. Muscle force variation of the rectus femoris was computed. Peak-to-peak (i.e., difference between maximal values) rectus femoris forces showed deviations of 55[Formula: see text]N and 40[Formula: see text]N for the first and second peaks, respectively. The development of the new fusion operator and fusion-based probability-box leads to a more consistent uncertainty quantification. This allows the estimation of risks associated with the simulation outcomes under input data uncertainties for rigid musculoskeletal modeling and simulation.