## 5 Tomographic Detection of Carotid Plaque and Artificial Intelligence
### AI and CTA for Symptomatic Plaque  
In a recent study, [PERSON] et al. [122] used a DL model for subtyping an atherosclerotic plaque stability phenotype on carotid CTA in comparison with histologic specimens from carotid endarterectomy. A CNN was trained to identify plaque as minimal, stable, or unstable. After training, the algorithm was validated in an unseen dataset, showing strong agreement with pathologist classification and high accuracy for each plaque phenotype recognition.  
Vascular imaging plays an important role in the management of carotid artery stenosis. For example, CTA is frequently performed to confirm the diagnosis and identify the correct surgical treatment and follow-up. Several studies showed that plaque classification that includes qualitative characteristics beyond luminal stenosis could predict future cerebral ischemic events with great accuracy [123].  
In this regard, radiomic characteristics were extracted from carotid CT scans in a study of patients with recent ischemic stroke to investigate their accuracy and ability to identify high-risk culprit carotid arteries from non-culprit carotid arteries. The authors combined ElasticNet logistic regression for the identification of culprit lesions using a model with 93 radiomic-derived variables with great accuracy to simulate inter-observer variability for region-of-interest demarcation, and they obtained an AUC of 0.73 [124].  
Moreover, [PERSON] et al. developed a machine learning model to identify high-risk carotid plaques by mixing non-imaging-based characteristics and non-invasive imaging-based characteristics. An accuracy of 0.76 was achieved thanks to the relief feature selection technique and the support vector machine classification scheme [125].  
In another study, the same authors [126] proposed a machine learning model for the diagnosis and detection of asymptomatic carotid artery stenosis, training the model with frequent demographics, clinical data, risk factors, and pharmacological data. The final model was able to classify the patients into high-risk (Class 1-CAS group) and low-risk (Class 0-non CAS group) individuals. The authors obtained good accuracy (0.82) and area under the curve (0.9) using the relief feature selection technique and the random forest classification scheme. Concerning carotid symptomatic plaques, a recent study assessed the good accuracy of AUTO Stroke Solution large vessel occlusion (LVO) detection application in acute ischemic stroke patients [127]. Additionally, the authors compared the algorithm's accuracy with clinical experts in identifying anterior circulation occlusions. Unfortunately, a strong limitation was that no posterior circulation or medium vessel occlusion patients were considered in the study as the deep learning algorithm was extremely dependent on the training data utilized.  
In a retrospective analysis [128] conducted on 610 CTAs, the performance of an AI-based algorithm for LVO detection in acute ischemic stroke was assessed. The Viz-LVO model identified occlusions of the internal carotid artery terminus and middle cerebral artery with high sensitivity (87.6%) and specificity (88.5%) and an accuracy of 87.9% (AUC 0.88, 95% CI: 0.85-0.92, \(p<0.001\)). Interestingly, the mean run time of the algorithm was less than 3 min, enabling quick decision-making for reperfusion treatments.