## 4 Artificial Intelligence in Ultrasound in Symptomatic Carotid Plaque
### Application of AI for Vulnerability Plaque Assessment  
In another study, the application of deep learning models (in particular CNN-based models and U-Net) using 2D ultrasound imaging was a suitable methodology for the automated classification of plaques into vulnerable and stable lesions [15]. Furthermore, [PERSON] et al. [75] conducted a study to propose a new model based on ultrasound images in combination with a LASSO algorithm to assess the stability of atherosclerotic plaques using ultrasound images. The study population comprised 150 patients with acute ischemic stroke or transient ischemic attack in whom carotid plaques were detected using ultrasound and high-resolution magnetic resonance examination. The model was created based on four conventional ultrasound carotid features: surface morphology, expression of the fibrotic cap, echogenicity, and ulceration, identifying 83 vulnerable and stable plaques, with an accuracy, sensitivity, and specificity of 79.05, 85.94, and 68.29%, respectively. In addition, the logistic regression model was used to create a conventional ultrasound-texture feature combined model for the automated identification of stable and unstable plaques: the combined model had a high accuracy, sensitivity, and specificity (84.76, 77.94, and 97.30%) and was based on eight statistically significant features extracted from the ultrasound images of carotid plaques. Unfortunately, the study presents some limitations, such as selection bias derived from the bidirectional cohort study; bias derived from the manual measurement of the plaques; the relatively small size sample; and the lack of histopathological confirmation [98]. Moreover, an optimized system (DCNN11A5) [99] using the mean of the feature strength (MPS) has been proposed for computed plaque characterization, distinguishing symptomatic and asymptomatic plaque based on echogenic characteristics and surface regularity. This model was proposed in a study by [PERSON] et al. conducted on a population cohort of 346 patients (196 patients with symptomatic plaque and 150 with asymptomatic plaque) whose images from internal carotid arteries were obtained by an echocolor Doppler study. 36 Diffusion convolutional neural network (DCNN) combinations were analyzed to find the best DCNN model under augmentation, showing that 3D optimization (called DCNN11A5) resulted in the best-performing model. This model was able to distinguish symptomatic and asymptomatic plaques based on the MFS, finding higher values in the first than the latter condition with an accuracy of 95.66% (AUC 0.956, \(p<0.0001\)). The DCNN11A5 model appeared to be more accurate than other ML methods, providing an improvement of 7.01% (\(p<0.0001\)) in the identification of plaque vulnerability. In the same study, bispectrum strength computation was used by applying the Radon transform to all the US images collected for the calculation of the B value in the two groups of patients; in particular, B values in symptomatic plaques were 5.4% higher than in asymptomatic plaques. In conclusion, the study proposed two new automated models which are able to provide an additional useful tool to support the need for invasive therapeutic strategies (endarterectomy or stenting) on the basis of plaque characteristics linked to vulnerability risk. The small size of the cohort and the limited availability of the supercomputer during the study affected the strength of the results of the study. Given the role of hemodynamic factors on the stability and the progression of plaques, a combined computational 3D geometry technology based on carotid artery ultrasound data collected in hospital practice was created to identify complex flow features in the case of critical carotid stenosis [99]. In one study performed on a population cohort of 346 patients (196 patients with symptomatic plaque and 150 with asymptomatic plaque) whose images from internal carotid were obtained by Doppler study, the MFS values in symptomatic plaque were higher than in asymptomatic plaques.