## 4 Artificial Intelligence in Ultrasound in Symptomatic Carotid Plaque
### AI Based on US Images for Estimation of CVD Risk  
Interestingly, deep networks have recently been applied to create automated models for the study of vascular morphology, starting from images derived from the segmentation of the media-adventitia boundary and lumen-intima boundary interface in transverse and longitudinal sections. The combination of these US images and a single-layer perceptron network preceded by an autoencoder was proposed by [PERSON] to estimate the intima-media thickness in an automated way [93].  
Deep CNNs found application in the plaque-burden estimation of the media-adventitia boundary and lumen-intima boundary by an extensive dataset constituted of symptomatic and asymptomatic transverse carotid ultrasound images. In one study performed on a population of 15 people with different grades of carotid stenosis (between 0 and 60%), the application of a deep CNN model to examine small features from a small portion of the whole US image through the convolutional layers was used to create a fully automated approach with a good performance in the automatic measurement of the media-adventitia boundary and lumen-intima boundary (94 and 91%) [108].  
AtheroEdge(tm) software, a fully convolutional network-based system developed according to the evaluation of the vascular contour in US images, has proven to be accurate in estimating the plaque area by tracing the distance between the lumen-intima and media-adventitia borders [109].  
The sonographic study of the carotid wall is part of the assessment of cardiovascular risk. In fact, the intima-media thickening (IMT) and area of the plaque are associated with a higher cardiovascular and stroke risk. However, most of the models for risk assessment using AI have been created based on the analysis of conventional cardiovascular risk factors. Though the major application of AI based on US carotid images has been used in a diagnostic role, ML algorithms combining imaging phenotypes with or without clinical features have been proposed for the estimation of prognosis in patients with high cardiovascular risk [110].  
[PERSON] et al. [111] proposed two machine learning models (AtheroEdge-MCDLAI: AE3.0 DL and AtheroPoint(tm)) correlating the carotid plaque characteristics on ultrasound study (IMT, area of plaque, maximum plaque height, and neovascularization evaluated after contrast microbubbles injection) and cardiovascular risk in 459 patients. The study demonstrated a higher performance of the ML algorithm in predicting cardio- and cerebrovascular events than conventional approaches based on clinical features. The study was limited due to the small size of the cohort, the higher baseline cardiovascular risk of the population study, and the lack of evaluation of the lipidic profile.  
In conclusion, a combination of AI using DL model-based carotid ultrasound imaging frameworks and conventional risk factors may increase the accuracy in predicting cardiovascular disease compared with conventional risk calculators. Moreover, more studies validated on the use of contemporary databases are required to increase the reliability and robustness of the applicability of AI in the assessment of cardiovascular risk.  
The application of artificial intelligence using US images offers a new strategy for the rapid and objective diagnosis of carotid atherosclerosis. Some limitations vitiate this new method: the performance of deep learning could be affected by the presence of noise or redundant information; the studies have been carried out on a small-moderate-sized cohort; the US images were obtained from static scans, while motion imagery has usually not been used for attempting to study intima-media thickening/atherosclerotic plaques; and semi-automated methods still require the participation of physicians.