Computational framework for population-based evaluation of TKR-implanted patellofemoral joint mechanics
Azhar A Ali1, Chadd W Clary1, Lowell M Smoger1
1Center for Orthopaedic Biomechanics, Mechanical and Materials Engineering, University of Denver, 2155 E Wesley Ave, Denver, CO, 80208, USA.
Biomechanics and Modeling in Mechanobiology
|February 6, 2020
Summary
Patient anatomy significantly impacts knee replacement mechanics. This study developed a computational workflow to evaluate implant performance across diverse patient anatomies, optimizing total knee replacement design for better outcomes.
Area of Science:
- Biomechanics
- Medical Device Design
- Computational Modeling
Background:
- Patient anatomy variations influence joint mechanics, a critical factor in total knee replacement (TKR) implant design.
- Understanding intersubject anatomical variation is essential for developing effective TKR implants.
Purpose of the Study:
- To create a computational workflow for population-based TKR implant mechanics evaluation.
- To assess efficient sampling strategies for TKR design phase screening.
- To analyze the relationship between patient-specific anatomy and TKR performance.
Main Methods:
- Generated virtual knee anatomies using a statistical shape model.
- Performed virtual implantation, sizing, and alignment of TKR components.
- Utilized finite-element analysis to simulate deep knee bend and predict patellofemoral (PF) mechanics.
Main Results:
- Predicted performance bounds for TKR kinematics and contact mechanics.
- Identified relationships between anatomical factors (e.g., alta patellar alignment) and PF mechanics.
- Observed increased PF range of motion with larger femoral component size.
Conclusions:
- Latin Hypercube sampling is effective for initial TKR design screening.
- The developed workflow supports robust TKR design by considering anatomical variations.
- Computational evaluation of anatomical variation enhances implant design and predicts performance bounds.
