## 4 Discussion  
The present systematic review followed the PRISMA guidelines with the objective of understanding which numerical techniques have been applied to simulate the blood and aortic wall interaction in healthy and diseased aortas, and the intrinsic reasons behind the lack of implementation of these models in real-life clinical practice were explored. The gathered results support the ability of numerical models to provide novel insightful information on the development and growth of several pathological conditions, such as ATAA [12; 27] or AD [10; 64; 89]. Numerical simulations assisted by medical imaging data showed capabilities of estimating in vivo aortic wall material properties [91; 93] and WSS [41; 80] and WSS distributions [29; 39]. Furthermore, several groups proved that simple geometrical criteria cannot reliably predict the risk of acute events [31; 52] and suggested stronger haemodynamic [78; 97] and structural [37; 106] metrics for acute complications' risk assessment. However, these tools are not widely implemented in clinical practice, and three aspects were identified as the most relevant to the lack of presence in real-life medical applications of numerical modelling. The first aspect is the need for higher levels of accuracy in the numerical results. Numerical modelling is highly impacted by the chosen initial conditions (e.g., initial geometric model, definition of the fluid and solid domains) and applied BC. To systematically produce proper results, these conditions must be rigorously selected and as close as possible to the in vivo patient-specific conditions. This necessity was evidenced by [PERSON] and [PERSON] [44], [PERSON] et al. [33] and [PERSON] et al. [27], which have shown that numerical results are significantly affected by slight variations in inlet BC, either in spatial or time distributions. [PERSON] et al. [28] and [PERSON] et al. [14] proved that patient-specific constitutive parameters induce closer agreement between numerical results and in vivo data. On one hand, we are still lacking a concrete understanding of which biomechanical features of diseased and healthy aortas are relevant to be modelled. For instance, in Section 3.7, we discussed the contradictory evidence that was found on modelling the flow rheology [23, 110], turbulence [111, 110, 112] and wall dynamics [40, 73]. Another contributor to inaccuracies is the often disregarded of features that may significantly impact the numerical results, such as wall thickness and mechanical properties' heterogeneity, aortic root displacement and external pressure exerted by surrounding vasculatures (e.g., pulmonary trunk and superior vena cava). For instance, evidence of increased longitudinal stress at the ascending aortic outer curve when modelling realistically heart motion was presented by [PERSON] et al. [13]. Nonetheless, more work is needed to address the real impact of including such simplifications. On the other hand, to this day, it is still difficult to estimate in vivo patient-specific parameters required to model the structural behaviour of the aortic wall as well as inlet and outlet BC. Hence, it represents a complex task to assist numerical calculations with accurate initial conditions, which is crucial to achieving good numerical results. Great efforts have been presented to improve the accuracy of numerical models and one common idea is to allow the model to estimate these requirements through the use of optimisation algorithms. Take as an example the works of [PERSON] et al. [94] and [PERSON] and [PERSON] [5], which implemented inverse methodologies to estimate aortic material properties and inlet and outlet BC from MRI data, respectively. MRI and CT data usually present noise and suffer from low time and spatial discretisation, which also act as a source of uncertainties in the results. Therefore, even using these inverse methodologies, which nowadays seem to represent the best solution, can still lead to an offset between the estimated and real parameters.