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Rheumatoid Arthritis: Atherosclerosis Imaging and Cardiovascular Risk Assessment Using Machine and Deep Learning-Based Tissue Characterization.

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.

journal_name

Curr Atheroscler Rep

journal_title

Current atherosclerosis reports

authors

Khanna NN,Jamthikar AD,Gupta D,Piga M,Saba L,Carcassi C,Giannopoulos AA,Nicolaides A,Laird JR,Suri HS,Mavrogeni S,Protogerou AD,Sfikakis P,Kitas GD,Suri JS

doi

10.1007/s11883-019-0766-x

subject

Has Abstract

pub_date

2019-01-25 00:00:00

pages

issue

eissn

1523-3804

issn

1534-6242

pii

10.1007/s11883-019-0766-x

journal_volume

pub_type

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Rheumatoid Arthritis: Atherosclerosis Imaging and Cardiovascular Risk Assessment Using Machine and Deep Learning-Based Tissue Characterization.