## 4 Artificial Intelligence in Ultrasound in Symptomatic Carotid Plaque
### Application of AI for Vulnerability Plaque Assessment
In the same study, the application of bispectrum strength computation confirmed that symptomatic plaques were more hypoechoic and patchier than asymptomatic ones. This model showed an accuracy of 95.66% and can help to guide surgeons' decisions to better therapeutic strategy [99]. In another study by [PERSON] et al. [100], a structured hexahedral mesh using grid generation commercial software FEMAP (Siemens PLM software) was constructed to automatically evaluate three hemodynamic descriptors: the time-averaged wall shear stress, the oscillating shear index, and the relative residence time. The population study was set up of one patient with carotid stenosis >50% and one healthy control; both underwent an echocolor study to outline the vessel wall and the plaque profile and a Doppler analysis to evaluate the values of flow velocity during different phases of the cardiac cycle in the enlarged bulb and distal zone. In particular, high velocities (peak systolic velocity and medium diastolic velocity) were detected throughout the whole area around the bulb and bifurcation in patient P1 compared with patient P2. Furthermore, higher wall shear stress values were found in the proximal tract of ICA in P1 (30-42 kPa), while lower values (22 kPa) were detected in P2 in correspondence with ECA, suggesting prevalent turbulence in the case of stenosis. In conclusion, the automated computational fluid dynamics model was able to detect a high wall shear stress associated with carotid stenosis, particularly during the systolic phase, compared with the non-stenosis case. In the first patient, automated computational fluid dynamics analysis detected a high wall shear stress, particularly during the systolic phase, compared with the non-stenosis case. Moreover, the method was able to identify deficiencies in Doppler velocimetry in the case of high-grade stenosis responsible for aliasing or in the case of irregular calcified plaques where artifacts hide the true lumen of the carotid artery. The limitations of the study were the rigid walls linked to the age of the patients; the possibility of errors in the estimation of flow blood patterns due to the straight flow extensions; and variability in the carotid geometry. Intraplaque neovascularization (IPN) is another risk determinant for plaque vulnerability predisposing to lesion rupture [101; 102]. IPN can be detected with contrast-enhanced ultrasound (CEUS) appearing as small contrast spots moving into and outside the atherosclerotic plaque. The intravascular localization, the small sizes of the plaques, and the presence of artifacts make IPN diagnosis difficult. For this reason, the development of methods using AI for the detection of IPN has been proposed. [PERSON] et al. [103] proposed an ML software (CINQS) able to identify IPN using 45 CEUS images of carotid plaques from 29 patients. The model was able to identify the two best parameters (time-integrated IPN surface area and statistical segmentation-based IPN surface area) for the diagnosis of IPN, showing a high accuracy (AUC \(>\) 0.9) in the identification of vulnerable plaques. The limitations of the study were the use of consensus visual IPN scoring, the small size of the cohort, and the lack of histopathological confirmation. Another factor associated with the vulnerability of atherosclerotic lesions is plaque carotid motion. In fact, during the cardiac cycle, carotid atherosclerotic plaques exhibit a multi-directional motion due to arterial displacement. Carotid motion is associated with plaque vulnerability: in particular, longitudinal displacement is responsible for a higher risk of plaque rupture than horizontal motion [104]. Different motion patterns have been described [77; 105], and asynchronism between plaque and adjacent carotid walls has been linked to an increased risk of cerebrovascular events [106]. Carotid motion can be detected by radiofrequency ultrasound and B-mode with the same accuracy and reproducibility [107].