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 proximal resorbed Bone Mass Fraction (BMF) [1] and implant-bone interface failure index [1], respectively, and the other based on initial micromotion, were formulated to model the multi-criteria optimization problem. The first two objective functions, e.g., objectives f1 and f2, were calculated from the FE analysis (Ansys), whereas the third objective (f3) involved an NN developed for the purpose of predicting the post-operative micromotion based on the stem design parameters. Bonded interfacial condition was used to account for the effects of stress shielding and interface stresses, whereas a set of contact models were used to develop the NN for faster prediction of post-operative micromotion. A multi-criteria GA was executed up to a desired number of generations for optimization (Fig. 1). The final trade-off model was further evaluated using a combined remodelling and bone ingrowth simulation based on an evolutionary interface condition [2], and subsequently compared with a generic TriLock implant.

The non-dominated solutions obtained from the GA execution were interpolated to determine the 3D nature of the Pareto-optimal surface (Fig. 2). The effects of all failure mechanisms were found to be minimized in these optimized solutions (Fig. 2). However, the most compromised solution, i.e., the trade-off stem geometry (TSG), was chosen for further assessment based on evolutionary interfacial condition. The simulation-based combined remodelling and bone ingrowth study predicted a faster ingrowth for TSG as compared to the generic design. The surface area with post-operative (i.e., iteration 1) ingrowth was found to be ∼50% for the TSG, while that for the TriLock model was ∼38% (Fig. 3). However, both designs predicted similar long-term ingrowth (∼89% surface area). The long-term proximal bone resorption (upto lesser trochanter) was found to be ∼30% for the TSG, as compared to ∼37% for the TriLock model. The TSG was found to be bone-preserving with prominent frontal wedge and rectangular proximal section for better rotational stability; features present in some recent designs. The optimization scheme, therefore, appears to be a quick and robust preclinical assessment tool for cementless femoral implant design.

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