Key result
Artificial intelligence models, particularly transfer learning, achieved high accuracy (94.55%) and AUC (0.946) in classifying symptomatic versus asymptomatic carotid plaque.
Why the study?
Atherosclerotic plaque in carotid arteries can lead to cerebrovascular events if not monitored, motivating the design and evaluation of multicenter AI-based tissue characterization and classification systems.
Can AI-based systems accurately characterize and classify carotid ultrasound plaque tissue as symptomatic or asymptomatic?
Can AI-based systems accurately characterize and classify carotid ultrasound plaque tissue as symptomatic or asymptomatic?
AI-based tissue characterization and classification systems demonstrate high accuracy in differentiating symptomatic from asymptomatic carotid plaques on ultrasound.
Cautions against clinical adoption of AI plaque classifiers; leaves open prospective validation in outcome trials.
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 (pMFS (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.
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Saba et al. (2021) studied Atherosclerotic plaque in carotid arteries. Artificial intelligence-based tissue characterization and classification systems was evaluated on Mean accuracy and AUC for plaque classification. Artificial intelligence models, particularly transfer learning, achieved high accuracy (94.55%) and AUC (0.946) in classifying symptomatic versus asymptomatic carotid plaque.
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