A high stem stiffness means that most of the load is transferred from the prosthetic head to the distal femur by the stem itself. So, the bone tissue load in the epiphyseal region of the proximal femur is significantly lower than the physiological load. This effect is called ‘stress shielding’. Therefore, a structure with proper stiffness should be selected as the implant to prevent the stress-shielding effect caused by high stiffness [6]. Ścigała et al. [6] used internal lattice structures to reduce the stiffness of the hip endoprosthesis. They designed new structures and used the finite element method to analyze the stiffness of the implants. Their results showed that the use of inner lattice structures reduced implant stiffness and therefore potentially avoided the stress-shielding effect (Figure 1).

The in silico, in vitro, and in vivo testing approaches are the three main types of method used to evaluate the performance of joint replacements.

The in silico method, also called the numerical method, uses the computational models to numerically evaluate the performance of a joint replacement. Once it is developed and validated, it can be efficiently used to evaluate the replacement’s performance. The numerical models developed in the literature can be classified into three groups. First, the musculoskeletal models are used. The musculoskeletal models are the models using rigid bones, muscles lines, etc., to simulate the activities of the human body. The models can be used to quickly evaluate the influence of the geometry of the replacements on the performance during daily activities, such as stair climbing, squatting, etc. Navacchia et al. [44] proposed a solution to address the inevitable tradeoff between computational cost and model detail in musculoskeletal simulations. However, because the musculoskeletal models are simplified as rigid models, detailed information regarding the stress and strain distribution within cannot be obtained, so the wear mechanism cannot be fully explored. To tackle these issues, the second approach, i.e., the coupled musculoskeletal-FE model, is used. The musculoskeletal-FE model uses rigid segments to represent the human body and uses the FE model to represent the part that is of great interest. The boundary conditions are passed from the musculoskeletal model to the FE model so that the FE model can be used to simulate different stages of daily activities. The advantage of this method is that both the overall and local behaviors in the joint replacement and the human tissues can be evaluated. However, defining the appropriate boundary conditions in the FE model is a challenging issue. The musculoskeletal model is a simplified model, and thus the boundary conditions obtained from it may not be accurate and appropriate. To solve this problem, FE models of the human body have been developed. FE models directly use the FE technique to simulate the daily activities of the human body. The challenge for this technique is to simulate the active contraction of the skeletal muscles, which is the driving force for musculoskeletal movements. The advantage of this approach is that the entire human segments are modeled using the deformable body and, consequently, the detailed mechanical information (stress, strain, etc.) can be obtained in every location within the model. However, because the detailed FE model is built, the computational cost is very large in the simulation of daily activities. Using this technique, Navacchia et al. [44] developed a computationally efficient muscle-force prediction strategy to track gait and chair rise experimental joint motion with a finite element of the musculoskeletal model of the lower limb.