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

Abstract

The shape and geometry of femoral implant influence implantinduced periprosthetic bone resorption and implantbone 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 tradeoff between these two conflicting objectives. The objective of this study was to develop a novel multiobjective custombased shape optimization scheme for cementless femoral implant by integrating finite element (FE) analysis and a multiobjective genetic algorithm (GA). The FE model of a proximal femur was based on a subjectspecific CTscan dataset. Eighteen parameters describing the nature of four key sections of the implant were identified as design variables. Two objective functions, one based on implantbone 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 tradeoff 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.