## 4 Discussion
However, based on our experience, these FE models can be solved utilizing parallel computing on powerful computer servers, although limitations arise related to licensing of commercial software. Thus, it would be valuable to hear from researchers in this field regarding their experiences and suggestions for alternative strategies. For expediting the post-processing, it is highly dependent on the research question. When studying the effect of uncertainties in models, e.g., from material properties, the type of parametric study chosen will considerably impact the time necessary for the completion of the analyses [35]. On the other hand, when iterative processes are not needed but largecohorts are studied, automating the analysis can easily be implemented by linking FE software outputs with programming languages such as Python and MATLAB. To verify OA prediction results, the best approach is comparing predictions against degenerative tissue changes obtained from clinical imaging during a follow-up period. This can be a challenging task since the analysis of morphological changes may be subjective and takes a great deal of time, especially when data from thousands of subjects are required. This exposes the necessity for novel methods to produce objective data to verify prediction results. As an example to overcome that limitation, automated approaches to measuring cartilage thickness from clinical image data have been developed [182]. This method could be utilized to generate feasible data (changes in cartilage thickness between baseline and follow-up imaging) to verify novel degenerative and adaptive algorithms. To account for patient-specific tissue composition information to study knee OA, imaging is a well-developed field in medicine, with continuous improvements in equipment and post-processing tools [183], that can be linked to the FEA workflow. Mapping MRI markers have demonstrated good correlations with early OA location and progression. These markers include T\({}_{2}\) and T\({}_{1\theta}\) relaxation times, which are related to water content, collagen distribution, and proteoglycans density [184, 185, 186], which can be considered as FE model inputs. Early in this review, we highlighted how [PERSON] et al. [86, 87] used \({}^{23}\)Na-MRI information as input data for FEAs of the knee. Though the distribution of MRI parameters can be transferred into the distribution of mechanical parameters in FE models, the workflow behind this translation is time-consuming. Recent studies have made efforts to rapidly produce MRI marker distributions [187, 188], but the approach to automatically translating them into FE models is still missing. As subchondral bone has a contribution to the development of knee OA [189, 190], it should also be considered when making more accurate FE models to predict disease progression. In this line of research, [PERSON] et al. [191] developed a methodology to correlate bone density and texture with OA progression from X-ray images with low clinical post-processing, offering a rapid and low-cost analysis. Bone information can be incorporated in the FEA workflow, either for site-specific inputs or for verification purposes. As mentioned earlier, in vivo estimation of tissue mechanical properties in the knee joint is challenging, making it difficult to include complex patient-specific tissue information in FE models. However, in vivo tissue stiffnesses can be estimated using techniques including MRI [185, 192], based on strain-stress relationships, and ultrasound [88], based on wave velocities in media. [PERSON] et al. [193] used shear wave elastography (SWE) to determine [PERSON]'s moduli of muscles, ligaments, and tendons around the knee joint. The study involved 50 female and male subjects, divided into groups aged 21-30, 31-40, 41-50, and above 51 years. The authors found higher values for males and decreasing values for older subjects, but the mechanisms behind the decrease in the mechanical properties due to aging are still unknown.