## 3 Results
### Risk Assessment Strategies and Diagnosing Techniques
#### 3.6.2 Risk Estimation of Aneurysm Rupture  
The rupture phenomenon of aneurysms was studied in six different works [34; 97; 104; 105]. [PERSON] et al. [105] experimentally (silicone test samples) and in silico tested idealised models of the abdominal aorta until rupture. Inflation tests were virtualised resorting to CSM simulations. The aim was to identify the probable location of rupture in AAA using Von Mises stress and PWS criteria. Material properties were identified by fitting different constitutive models with uniaxial tensile test data. The third-order Ogden model was found to be the constitutive model that produced better approximations (\(R^{2}=0.9812\)). Numerical results showed that higher stress appeared at regions of higher strain rather than higher diameter. The PWS rupture criteria presented good agreement with experimental data. [PERSON] et al. [34] performed CSM simulations on patient-specific DTAA models with the objective of assessing the correlation between aneurysm growth and PWS. This metric presented a stronger correlation with aneurysm growth than maximum diameter and was suggested as a possible indicator of acute complications. [PERSON] et al. [106] developed a numerical model using CFD assisted by MRI data to assess the influence of abnormal haemodynamics on rupture risk. They analysed both numerical results and experimental data from bulge inflation tests. Higher flow eccentricity was correlated with higher and more disturbed WSS distributions. The areas of higher WSS were coincident with the region of flow impingement and also correlated with higher values of TAWSS and OSL which were previously correlated with a high probability of rupture [78]. However, a direct correlation between WSS and wall strength was not found, possibly suggesting that further structural analysis is also required. On the 31 analysed patients with CFD simulations by [PERSON] et al. [101], the authors found higher WSS magnitude and pressure index in all ascending aorta for BAV patients and evidence of PWS being a great candidate to predict the risk of ATAA rupture. Moreover, [PERSON] et al. [97] assessed correlations between biomarkers (e.g., MMP, TIMP and miRNA), WSS (obtained via CFD) and aortic strain (obtained from post-processing CT data) in ATAA. Evidence of higher WSS and wall strain from the STJ to the mid-ascending aorta was presented. The increase in WSS was correlated with higher matrix metalloproteinase synthesis, which can dysregulate the homeostasis of the aortic wall and promote elastin and collagen degradation. A combination of biomarkers (extracted from blood samples) and non-invasive evaluation of WSS distributions may assist in the rupture risk assessment of ATAA. The use of ML tools to estimate rupture risk in ATAA was reported in three articles. [PERSON] et al. [58] used a similar technique to [PERSON] et al. [91] and [PERSON] et al. [105], in order to fit constitutive models to tension-strain data from bulge inflation tests. The estimated material parameters and geometric parameters were analysed by the developed ML tool, which classified the data into rupture and non-rupture groups. This analysis suggested that the members of the ruptured group presented similar material properties. One important remark is that the model identified strong correlations between tissue strength and pre-rupture response features. [PERSON] et al. [57] developed two ML models which were also trained with tension-strain data collected through ex vivo inflation tests on ATAA tissue samples (rupture was ensured). The first aimed to identify the locations of higher tension buildup along the aortic wall. The second estimated ultimate tensile stress in the identified regions. The results suggested that local rupture strength can be reliably estimated from the pre-rupture features, as postulated by [PERSON] et al. [58], matching 13 out of 15 estimated rupture sites with experimental data [56]. [PERSON] et al.