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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), comp...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
AME Publishing Company
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8350643/ https://www.ncbi.nlm.nih.gov/pubmed/34430647 http://dx.doi.org/10.21037/atm-20-7676 |
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author | Saba, Luca Sanagala, Skandha S. Gupta, Suneet K. Koppula, Vijaya K. Johri, Amer M. Khanna, Narendra N. Mavrogeni, Sophie Laird, John R. Pareek, Gyan Miner, Martin Sfikakis, Petros P. Protogerou, Athanasios Misra, Durga P. Agarwal, Vikas Sharma, Aditya M. Viswanathan, Vijay Rathore, Vijay S. Turk, Monika Kolluri, Raghu Viskovic, Klaudija Cuadrado-Godia, Elisa Kitas, George D. Sharma, Neeraj Nicolaides, Andrew Suri, Jasjit S. |
author_facet | Saba, Luca Sanagala, Skandha S. Gupta, Suneet K. Koppula, Vijaya K. Johri, Amer M. Khanna, Narendra N. Mavrogeni, Sophie Laird, John R. Pareek, Gyan Miner, Martin Sfikakis, Petros P. Protogerou, Athanasios Misra, Durga P. Agarwal, Vikas Sharma, Aditya M. Viswanathan, Vijay Rathore, Vijay S. Turk, Monika Kolluri, Raghu Viskovic, Klaudija Cuadrado-Godia, Elisa Kitas, George D. Sharma, Neeraj Nicolaides, Andrew Suri, Jasjit S. |
author_sort | Saba, Luca |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-8350643 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | AME Publishing Company |
record_format | MEDLINE/PubMed |
spelling | pubmed-83506432021-08-23 Multimodality carotid plaque tissue characterization and classification in the artificial intelligence paradigm: a narrative review for stroke application Saba, Luca Sanagala, Skandha S. Gupta, Suneet K. Koppula, Vijaya K. Johri, Amer M. Khanna, Narendra N. Mavrogeni, Sophie Laird, John R. Pareek, Gyan Miner, Martin Sfikakis, Petros P. Protogerou, Athanasios Misra, Durga P. Agarwal, Vikas Sharma, Aditya M. Viswanathan, Vijay Rathore, Vijay S. Turk, Monika Kolluri, Raghu Viskovic, Klaudija Cuadrado-Godia, Elisa Kitas, George D. Sharma, Neeraj Nicolaides, Andrew Suri, Jasjit S. Ann Transl Med Review Article on Carotid Artery Stenosis and Stroke-Prevention and Treatment Part II 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. AME Publishing Company 2021-07 /pmc/articles/PMC8350643/ /pubmed/34430647 http://dx.doi.org/10.21037/atm-20-7676 Text en 2021 Annals of Translational Medicine. All rights reserved. https://creativecommons.org/licenses/by-nc-nd/4.0/Open Access Statement: 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 (https://creativecommons.org/licenses/by-nc-nd/4.0/) . |
spellingShingle | Review Article on Carotid Artery Stenosis and Stroke-Prevention and Treatment Part II Saba, Luca Sanagala, Skandha S. Gupta, Suneet K. Koppula, Vijaya K. Johri, Amer M. Khanna, Narendra N. Mavrogeni, Sophie Laird, John R. Pareek, Gyan Miner, Martin Sfikakis, Petros P. Protogerou, Athanasios Misra, Durga P. Agarwal, Vikas Sharma, Aditya M. Viswanathan, Vijay Rathore, Vijay S. Turk, Monika Kolluri, Raghu Viskovic, Klaudija Cuadrado-Godia, Elisa Kitas, George D. Sharma, Neeraj Nicolaides, Andrew Suri, Jasjit S. Multimodality carotid plaque tissue characterization and classification in the artificial intelligence paradigm: a narrative review for stroke application |
title | Multimodality carotid plaque tissue characterization and classification in the artificial intelligence paradigm: a narrative review for stroke application |
title_full | Multimodality carotid plaque tissue characterization and classification in the artificial intelligence paradigm: a narrative review for stroke application |
title_fullStr | Multimodality carotid plaque tissue characterization and classification in the artificial intelligence paradigm: a narrative review for stroke application |
title_full_unstemmed | Multimodality carotid plaque tissue characterization and classification in the artificial intelligence paradigm: a narrative review for stroke application |
title_short | Multimodality carotid plaque tissue characterization and classification in the artificial intelligence paradigm: a narrative review for stroke application |
title_sort | multimodality carotid plaque tissue characterization and classification in the artificial intelligence paradigm: a narrative review for stroke application |
topic | Review Article on Carotid Artery Stenosis and Stroke-Prevention and Treatment Part II |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8350643/ https://www.ncbi.nlm.nih.gov/pubmed/34430647 http://dx.doi.org/10.21037/atm-20-7676 |
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