## 4 Discussion
Bone ingrowth on the partially coated cylindrical stem was absent when the micromotion criterion was set at 25 \(\upmu\)m, but some ingrowth was observed when the criterion was relaxed to 50 \(\upmu\)m. Micromotion thresholds of 50, 75, and 100 \(\upmu\)m have also been investigated by [PERSON] et al. [52], and it was shown that for higher micromotion thresholds, the predicted osseointegration was also higher. Some microscale models mimicking only local bone implant interfaces were proposed to investigate osseointegration and reduce the computational cost of the simulation [36; 39; 47; 48]. Although these models can more accurately and efficiently simulate the biochemical processes occurring at the interface, the influence of the anatomical topology of the bone, patient and implant variabilities, or loading scenarios on bone ingrowth would be limited. Loading cases are another crucial consideration for predicting the long-term stability of the implants. In particular, the iterative computational process necessitates careful selection of load cycle intervals; otherwise, it may yield inaccurate results [81]. Also, as we mentioned above, the loading cases for the microscale models were limited, mainly based on constant shear loads or tangential displacements. The hybrid method proposed in [33; 51] represents a good combination of the advantages of both macroscale and microscale modelling approaches: it integrates the results obtained by solving the macroscale model, thereby capturing the overall behaviour of the system, with the loading configuration considered from microscale models. For the macroscale models, the peak value of the hip joint force of normal walking and stair climbing was most widely used. Reducing the number of discretization sub-steps for the hip joint load resulted in accurate mechanical stimulus for osseointegration at the bone-implant interface [52], which could serve as a practical reference when considering methods to reduce computational expense. Although the effect of various loading cases, including different postoperative conditions, on bone ingrowth results has been analysed in a few studies [34; 38], we still have limited knowledge regarding which set of loading cases best predicts in vivo conditions. Also, one notable observation is that, to the best of the author's knowledge, no simulation has yet considered an abnormal gait cycle associated with various pathologies or conditions affecting the musculoskeletal system. In the future, it would be meaningful to investigate how abnormal gait patterns affect the results of osseointegration computational models. One last critical point worth mentioning is model validation. Traditional validation activities typically involve a rigorous comparison of the model predictions with the results obtained from the comparator (i.e., bench testing, clinical trials, animal experiments) [82]. For current bone osseointegration models, the existing literature acknowledges a lack of sufficient experimental and clinical data to robustly validate the computational models [36; 51]. Another big challenge for these FE models is not only accurately predicting the osseointegration process in vivo but also determining when it occurs and identifying whether it ultimately caused the implant to loosen. Very few studies provided quantitative comparative evidence in terms of bone ingrowth (e.g., percentage of the osseointegration area, fibrous tissue thickness) between the computational results and the clinical data [29; 30; 40]. A comparison between the relative micro-movements predicted by the models and the experimental measurements taken in vitro was mentioned in [42]. Qualitative agreement between simulation outputs and clinical observations was often used as model plausibility evidence. For example, a few authors found that bone ingrowth does not occur uniformly across the coated surface but rather partially [26; 30; 31].