Rheumatoid Arthritis: Atherosclerosis Imaging and Cardiovascular Risk Assessment Using Machine and Deep Learning-Based Tissue Characterization.
Curr Atheroscler Rep
; 21(2): 7, 2019 01 25.
Article
em En
| MEDLINE
| ID: mdl-30684090
ABSTRACT
PURPOSE OF THE REVIEW Rheumatoid arthritis (RA) is a chronic, autoimmune disease which may result in a higher risk of cardiovascular (CV) events and stroke. Tissue characterization and risk stratification of patients with rheumatoid arthritis are a challenging problem. Risk stratification of RA patients using traditional risk factor-based calculators either underestimates or overestimates the CV risk. Advancements in medical imaging have facilitated early and accurate CV risk stratification compared to conventional cardiovascular risk calculators. RECENT FINDING:
In recent years, a link between carotid atherosclerosis and rheumatoid arthritis has been widely discussed by multiple studies. Imaging the carotid artery using 2-D ultrasound is a noninvasive, economic, and efficient imaging approach that provides an atherosclerotic plaque tissue-specific image. Such images can help to morphologically characterize the plaque type and accurately measure vital phenotypes such as media wall thickness and wall variability. Intelligence-based paradigms such as machine learning- and deep learning-based techniques not only automate the risk characterization process but also provide an accurate CV risk stratification for better management of RA patients. This review provides a brief understanding of the pathogenesis of RA and its association with carotid atherosclerosis imaged using the B-mode ultrasound technique. Lacunas in traditional risk scores and the role of machine learning-based tissue characterization algorithms are discussed and could facilitate cardiovascular risk assessment in RA patients. The key takeaway points from this review are the following (i) inflammation is a common link between RA and atherosclerotic plaque buildup, (ii) carotid ultrasound is a better choice to characterize the atherosclerotic plaque tissues in RA patients, and (iii) intelligence-based paradigms are useful for accurate tissue characterization and risk stratification of RA patients.Palavras-chave
Texto completo:
1
Temas:
ECOS
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Aspectos_gerais
Bases de dados:
MEDLINE
Assunto principal:
Artrite Reumatoide
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Doenças das Artérias Carótidas
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Aterosclerose
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Aprendizado Profundo
Tipo de estudo:
Diagnostic_studies
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Etiology_studies
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Risk_factors_studies
Limite:
Humans
Idioma:
En
Revista:
Curr Atheroscler Rep
Assunto da revista:
ANGIOLOGIA
Ano de publicação:
2019
Tipo de documento:
Article
País de afiliação:
Índia