## 8 Discussion  
AI has shown great promise in improving the accuracy and efficiency of medical imaging interpretation. In recent years, there has been an increasing interest in the application of AI in carotid plaque detection using ultrasound, CT scans, and MRI. U-Net, a CNN model that uses contracting and up-sampling layers with skip connections, has been used for carotid segmentation with high accuracy (Dice coefficient 0.9) in a study by [PERSON] et al. [88] (see Table S1). Dilated U-Net, which combines U-Net with dilated convolution networks, has shown a better performance than U-Net alone in complex plaques [91]. However, semi-automated segmentation is required in dilated U-Net, which presents some limitations. Furthermore, the study by [PERSON] et al. has shown good accuracy in carotid wall segmentation using deep learning U-Net architecture for two different demographic groups of patients [97]. CNN-based models and U-Net have also been used to classify carotid plaques into vulnerable and stable lesions [21]. Interestingly, the optimized system,DCNN11A5, has been proposed for computed plaque characterization, distinguishing symptomatic and asymptomatic plaque with higher accuracy than other machine learning methods [99] (see Table S1). As this review demonstrates, the use of artificial intelligence in vascular imaging is an area of growing interest that has the potential to significantly impact clinical practice by improving the applicability, accuracy, and safety of tomographic acquisitions [116]. Multiple AI algorithms have been proposed to address various CTA pre-procedural problems, such as noise reduction, contrast dose and radiation reduction, and image quality control [117; 118; 119; 120]. Unsurprisingly, the majority of research using AI to detect and classify atherosclerotic plaques has been conducted on coronary atherosclerotic plaque, with promising results for the development of a non-invasive coronary computed tomographic angiography [121]. In a recent study, [PERSON] et al. used a deep learning (DL) model to subtype atherosclerotic plaque stability phenotypes on carotid CTA and showed strong agreement with pathologist classification and high accuracy for each plaque phenotype recognition [122]. Other studies have demonstrated the potential of AI and CTA for the management of carotid artery stenosis, such as plaque classification beyond luminal stenosis to predict future cerebral ischemic events [123] (see Table S2). Additionally, radiomic characteristics were extracted from carotid CT scans to identify high-risk culprit carotid arteries from non-culprit carotid arteries [124]. [PERSON] et al. developed a machine learning model to identify high-risk carotid plaques and achieved an accuracy of 0.76 [125]. They also proposed a machine learning model for the diagnosis and detection of asymptomatic carotid artery stenosis and obtained good accuracy and area under the curve [126] (see Table S2). Furthermore, DL algorithms have shown good accuracy in the detection of large vessel occlusions in acute ischemic stroke patients [127; 128]. AI has also been used to overcome technical issues in vascular imaging, such as image noise and artifact reduction [129], reconstruction model improvement [130], the automatic detection and localization of carotid plaques [131], and high-resolution CTA without contrast agent use [132]. [PERSON] et al. used DL-based software (MethinksLVO, Methinks Software S.L. Barcelona, Spain) to detect LVO in CT images and demonstrated a significant improvement in sensitivity and specificity for LVO diagnosis [133]. Overall, the use of AI in vascular imaging holds great promise for improving clinical outcomes and enhancing patient care. However, further research is needed to validate and refine these approaches for routine clinical use. In addition, this review underlines the advantages of using high-resolution MRI in plaque evaluation, including high soft-tissue contrast, the ability to combine multiple contrast weightings, lack of radiation, and operator independence.