A Genetic Algorithm Based Multi-Objective Shape Optimization Scheme for Cementless Femoral Implant

Abstract

The shape and geometry of femoral implant influence implant-induced periprosthetic bone resorption and implant-bone interface stresses, which are potential causes of aseptic loosening in cementless total hip arthroplasty (THA). Development of a shape optimization scheme is necessary to achieve a trade-off between these two conflicting objectives. The objective of this study was to develop a novel multi-objective custom-based shape optimization scheme for cementless femoral implant by integrating finite element (FE) analysis and a multi-objective genetic algorithm (GA). The FE model of a proximal femur was based on a subject-specific CT-scan dataset. Eighteen parameters describing the nature of four key sections of the implant were identified as design variables. Two objective functions, one based on implant-bone interface failure criterion, and the other based on resorbed proximal bone mass fraction (BMF), were formulated. The results predicted by the two objective functions were found to be contradictory; a reduction in the proximal bone resorption was accompanied by a greater chance of interface failure. The resorbed proximal BMF was found to be between 23% and 27% for the trade-off geometries as compared to ∼39% for a generic implant. Moreover, the overall chances of interface failure have been minimized for the optimal designs, compared to the generic implant. The adaptive bone remodeling was also found to be minimal for the optimally designed implants and, further with remodeling, the chances of interface debonding increased only marginally.

Microstructure Interpolation for Macroscopic Optimisation

“…Strength is an important requirement and it should be ensured that a bone prosthetic be able to withstand the rigours of day-to-day use over a long time period (Arabnejad Khanoki and Pasini, 2013). More suitable loading conditions, geometry and non-linear analysis can be seen in the works of Arabnejad Khanoki and Pasini (2013); Chanda et al (2015b); and Arabnejad Khanoki et al (2016a) for example, which consider multiple load cases on a more complicated geometry generated from CT data.…”

“…While more complicated and realistic domains have been considered (e.g. Chanda et al, 2015b) We note that the mesh used for describing the prosthetic is an implicit constraint and can impact the optimisation in subtle ways (Bendsøe and Sigmund, 2003). To reduce non-homogeneous inuences to the optimisation by the mesh a regular hexahedral mesh is used to describe the prosthetic design and solve the linear elastic problem.…”

“…Previously optimised designs either had no method to produce the optimised material properties for manufacture (Kuiper and Huiskes, 1997), were restricted to two dimensions (Arabnejad Khanoki and Pasini, 2012), or were restricted to homogeneous designs only (Chanda et al, 2015b).…”

“…Aseptic loosening leads to revision surgeries which are costly and cause patient discomfort. Mathematically, the chance of implant failure is typically quantied as a function of the stress on the interface between the prosthetic and the existing bone (Kuiper and Huiskes, 1997;Arabnejad Khanoki and Pasini, 2012;Chanda et al, 2015b), however fatigue has been considered (Arabnejad Khanoki and Pasini, 2013). A second major consideration in implant design is how much existing bone material will be removed by the body in response to changed loading conditions, termed bone resorption (Engh et al, 1992).…”

“…A plastic proof-of-concept was produced with additive manufacturing. Chanda et al (2015b) used a multi-objective genetic algorithm to optimise the shape of a femoral implant, also making use of CT data to provide the problem geometry. The optimised design variables were the shape of four elliptical cross sections allowing a fully solid, easily manufacturable design.…”