Multimodality carotid plaque tissue characterization and classification in the artificial intelligence paradigm: a narrative review for stroke application

Cardiovascular disease (CVD) is one of the leading causes of morbidity and mortality in the United States of America and globally. Carotid arterial plaque, a cause and also a marker of such CVD, can be detected by various non-invasive imaging modalities such as magnetic resonance imaging (MRI), computer tomography (CT), and ultrasound (US). Characterization and classification of carotid plaque-type in these imaging modalities, especially into symptomatic and asymptomatic plaque, helps in the planning of carotid endarterectomy or stenting. It can be challenging to characterize plaque components due to (I) partial volume effect in magnetic resonance imaging (MRI) or (II) varying Hausdorff values in plaque regions in CT, and (III) attenuation of echoes reflected by the plaque during US causing acoustic shadowing. Artificial intelligence (AI) methods have become an indispensable part of healthcare and their applications to the non-invasive imaging technologies such as MRI, CT, and the US. In this narrative review, three main types of AI models (machine learning, deep learning, and transfer learning) are analyzed when applied to MRI, CT, and the US. A link between carotid plaque characteristics and the risk of coronary artery disease is presented. With regard to characterization, we review tools and techniques that use AI models to distinguish carotid plaque types based on signal processing and feature strengths. We conclude that AI-based solutions offer an accurate and robust path for tissue characterization and classification for carotid artery plaque imaging in all three imaging modalities. Due to cost, user-friendliness, and clinical effectiveness, AI in the US has dominated the most

Prediction of the time period of stroke based on ultrasound image analysis of initially asymptomatic carotid plaques

Non-invasive ultrasound imaging of carotid plaques can provide information on the characteristics of the arterial wall including the size, morphology and texture of the atherosclerotic plaques. Several studies were carried out that demonstrated the usefulness of these feature sets for differentiating between asymptomatic and symptomatic plaques and their corresponding cerebrovascular risk stratification. The aim of this study was to develop predictive modelling for estimating the time period of a stroke event by determining the risk for short term (less or equal to three years) or long term (more than three years) events. Data from 108 patients that had a stroke event have been used. The information collected included clinical and ultrasound imaging data. The prediction was performed at base line where patients were still asymptomatic. Several image texture analysis and clinical features were used in order to create a classification model. The different features were statistically analyzed and we conclude that image texture analysis features extracted using Spatial Gray Level Dependencies method had the best statistical significance. Several predictive models were derived based on Binary Logistic Regression (BLR) and Support Vector Machines (SVM) modelling. The best results were obtained with the SVM modelling models with an average correct classifications score of 77±7% for differentiating between stroke event occurrences within 3 years versus more than 3 years. Further work is needed in investigating additional multiscale texture analysis features as well as more modelling techniques on more subjects