## 4 Discussion  
From this state-of-the-art review of the FE models predicting the AL failure, the main advantages and limitations of the different modelling approaches adopted by various authors have emerged. One of the most critical aspects when simulating the long-term stability of hip prostheses is modelling the complex osseointegration processes occurring at the femur-implant interface. The adaptative FE model was found to be a widely used technique to simulate osseointegration. Finite Element software such as Ansys and Abaqus was typically coupled with user subroutines programmed in Python or Matlab to iterate and integrate the calculations. Both bone remodelling and bone ingrowth, essential for maintaining the secondary stability of the cementless stem, should be included in the simulations. Since they are related processes in vivo (e.g., bone ingrowth is influenced by factors related to bone remodelling), most FE studies considered a combination of the two implemented within the same workflow loop [26; 27; 30; 31; 32; 38; 48]. Bone ingrowth and bone remodelling were considered two independent phenomena in a few models [26; 28]. \"Bone ingrowth\" refers to the growth of bone within the irregular surface of an implant or its intimate contact at the bone-implant interface following the biological response. \"Bone remodelling\" involves the removal of mineralised bone by osteoclasts (bone resorption/bone loss/osteolysis) followed by the formation of bone matrix through the osteoblasts that subsequently become mineralised (bone apposition or bone formation) [79; 80]. In this last process, the biological process of cell differentiation, also known as \"bone adaptation\", plays a crucial role. It is worth noting that bone remodelling was often simulated as bone mineral loss associated with the so-called stress shielding phenomenon. While bone resorption is indeed a biological process correlated with an increased risk of implant loosening, AL cannot be solely attributed to bone density loss. Therefore, this review focused more on papers that predicted bone ingrowth failure or the combined effects of the two processes. As also reported in Section 3.3, two main bone interface contact modelling techniques were identified and named as direct contact and indirect contact models. The first are more computationally efficient; however, regarding their main assumptions, some limitations should be pointed out. For example, in a few direct contact models, only one mechanical stimulus controls the change in contact status, and the outcome is such that once the contact behaviour is set to be bonded, it will remain in that state until the simulation process is complete [30; 31]. Certain direct contact models addressed this limitation by incorporating additional criteria for debonding checks [29; 32]. The indirect contact models involving spring elements also overcome this problem by varying the osseointegration level with different spring stiffness values [27]. The mechano-regulatory models and diffusion analyses of mesenchymal stem cells included in many indirect contact models that considered an interface layer enabled a more realistic mimicry of biological progressions and simulation of tissue differentiation processes [34; 36; 39; 48]. However, most of these models assumed that, even under initial post-operative conditions, the implant is surrounded by a layer of granulation tissue, whereas, in reality, the implant is in direct contact with the bone. Some uncertainties related to the definition of the micromotion threshold value used to estimate bone ingrowth were observed. For example, [PERSON] et al. [31] considered a threshold value of 50 \(\upmu\)m below which the contact status was set to bounded. [PERSON] et al. [29] adopted a similar approach with a 40 \(\upmu\)m micromotion threshold value. [PERSON] et al. [30] compared two threshold values of 25 and 50 \(\upmu\)m while determining whether the element was bonded or frictional. They showed a significant impact on the prediction of bone ingrowth.