Multiobjective design optimization of total knee replacements considering UHMWPE wear and kinematics

Total knee replacement is the gold standard treatment for restoring mobility and relieving pain associated with osteoarthritis when other medical therapy has failed. Revision surgery is necessary when the replaced knee fails, which is often a result of implant damage (such as wear) or poor kinematics.;It was hypothesized that a trade-off exists between wear and kinematics performance in total knee replacements. The objective of this research was to test this hypothesis by using multiobjective design optimization to describe this relationship with a Pareto curve. It was first necessary to develop and validate numerical frameworks for wear and kinematics simulations, using models constructed using a parametric modeller. The Pareto curve was then generated using a combination of single objective and multiobjective design optimizations considering these two performance measures.;Single objective optimization for wear yielded a theoretical design with superior wear resistance when compared to a typical commercially available knee design. Single objective optimization for kinematics yielded a theoretical design capable of higher flexion, as well as more natural laxity characteristics. After performing multiobjective design optimization, the resulting Pareto curve showed that there is, in fact, a tradeoff between wear and kinematics performance. When considering optimum designs, in order to improve the wear performance it was necessary to sacrifice kinematics performance, and vice-versa. This previously suspected but never verified nor quantified relationship can be used to improve total knee replacement designs, as well as help healthcare providers select the best implants for their patients.;Design optimization is a method for finding the best shape for a component using an optimization approach considering one or multiple performance metrics. The shape of a parametric candidate design can be manipulated by an optimization algorithm, which seeks to minimize an objective function subject to performance constraints and design space limitations. During multiobjective design optimization, multiple performance measures are minimized simultaneously, the relative importance of each determined using a weighted sum. This approach can also be used to derive a Pareto curve or frontier which graphically describes the relationships (or trade-offs) between the performance measures.