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

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• dc.contributor.author Saba, Luca
• dc.contributor.author Cuadrado-Godia, Elisa
• dc.contributor.author Suri, Jasjit S.
• dc.date.accessioned 2022-05-13T06:05:55Z
• dc.date.available 2022-05-13T06:05:55Z
• dc.date.issued 2021
• dc.description.abstract 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.
• dc.format.mimetype application/pdf
• dc.identifier.citation Saba L, Sanagala SS, Gupta SK, Koppula VK, Johri AM, Khanna NN, et al. Multimodality carotid plaque tissue characterization and classification in the artificial intelligence paradigm: a narrative review for stroke application. Ann Transl Med. 2021 Jul; 9(14): 1206. DOI: 10.21037/atm-20-7676
• dc.identifier.doi http://dx.doi.org/10.21037/atm-20-7676
• dc.identifier.issn 2305-5839
• dc.identifier.uri http://hdl.handle.net/10230/53069
• dc.language.iso eng
• dc.publisher AME Publishing Company
• dc.rights This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/4.0/
• dc.subject.keyword Stroke
• dc.subject.keyword Artificial intelligence
• dc.subject.keyword Cardiovascular disease (CVD)
• dc.subject.keyword Carotid imaging
• dc.subject.keyword Computer tomography (CT)
• dc.subject.keyword Magnetic resonance imaging (MRI)
• dc.subject.keyword Risk stratification
• dc.subject.keyword Ultrasound (US)
• dc.title Multimodality carotid plaque tissue characterization and classification in the artificial intelligence paradigm: a narrative review for stroke application
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/publishedVersion