Artificial Intelligence- and Radiomics-Based Evaluation of Carotid Artery Disease

Abstract

Recent research results on imaging of carotid artery disease are changing our perspective on the diagnosis and treatment of many conditions, in particular atherosclerotic disease. Research has been particularly focused on imaging features of plaque vulnerability to identify those patients at risk for stroke, independently of the conventional criteria, such as the degree of vessel stenosis.The application of artificial intelligence (AI) techniques to the study of carotid artery disease has dramatically increased in the recent years, allowing not only to automatically measure carotid artery stenosis but also to better identify features of plaque vulnerability. Furthermore, thanks to texture analysis and so-called radiomics, it has been possible to develop models for stroke risk stratification based on radiomics and clinical data.Although only few AI algorithms have been introduced into clinical practice, the pace of AI algorithm development is rapidly growing, and radiologists will be expected to implement such algorithms in the near future. In this chapter, the current knowledges about carotid artery imaging in clinical practice as well as the application of AI techniques and radiomics will be discussed.KeywordsVulnerable plaqueIntima-media thicknessDegree of stenosisModel of riskArtificial intelligenceRadiomics