SHAPE OPTIMIZATION OF FEMORAL IMPLANT BASED ON A MACHINE LEARNING FRAMEWORK AND ASSESSMENT OF THE OPTIMAL DESIGN USING EVOLUTIONARY INTERFACE

The success of a cementless Total Hip Arthroplasty (THA) depends not only on initial micromotion, but also on long-term failure mechanisms, e.g., implant-bone interface stresses and stress shielding. Any preclinical investigation aimed at designing femoral implant needs to account for temporal evolution of interfacial condition, while dealing with these failure mechanisms. The goal of the present multi-criteria optimization study was to search for optimum implant geometry by implementing a novel machine learning framework comprised of a neural network (NN), genetic algorithm (GA) and finite element (FE) analysis. The optimum implant model was subsequently evaluated based on evolutionary interface conditions.The optimization scheme of our earlier study [1] has been used here with an additional inclusion of an NN to predict the initial fixation of an implant model. The entire CAD based parameterization technique for the implant was described previously [1]. Three objective functions, the first two based on ...