A Multicenter Study on Carotid Ultrasound Plaque Tissue Characterization and Classification Using Six Deep Artificial Intelligence Models: A Stroke Application

Abstract

Atherosclerotic plaque in carotid arteries can ultimately lead to cerebrovascular events if not monitored. The objectives of this study are (a) to design a set of artificial intelligence (AI)-based tissue characterization and classification (TCC) systems (Atheromatic 2.0, AtheroPoint, CA, USA) using ultrasound-based carotid artery plaque scans collected from multiple centers and (b) to evaluate the AI performance. We hypothesize that symptomatic plaque is more scattered than asymptomatic plaque. Therefore, the AI system can learn, characterize, and classify them automatically. We developed six kinds of AI systems: four machine learning (ML) systems, one transfer learning (TL) system, and one deep learning (DL) architecture with different layers. Atheromatic 2.0 uses two types of plaque characterization: (a) an AI-based mean feature strength (MFS) and (b) bispectrum analysis. Three kinds of data were collected: London, Lisbon, and Combined (London + Lisbon). We balanced and then augmented five folds to conduct 3-D optimization for optimal number of AI layers versus folds. Using K10 (90% training, 10% testing), the mean accuracies for DL, TL, and ML over the mean of the three data sets were 93.55%, 94.55%, and 89%, respectively. The corresponding mean AUCs were 0.938, 0.946, and 0.889 ( $p ), respectively. AI paradigms showed an improvement by 10.41% and 3.32% for London and Lisbon in comparison to Atheromatic 1.0, respectively. On characterization, for all three data sets, MFS (symptomatic) > MFS (asymptomatic) by 46.56%, 19.40%, and 53.84%, respectively, thus validating our hypothesis. Atheromatic 2.0 showed consistent and stable results and is useful for carotid plaque tissue classification and characterization for vascular surgery applications.