Τίτλος:
Rheumatoid Arthritis: Atherosclerosis Imaging and Cardiovascular Risk Assessment Using Machine and Deep Learning–Based Tissue Characterization
Γλώσσες Τεκμηρίου:
Αγγλικά
Περίληψη:
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. Summary: 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. © 2019, Springer Science+Business Media, LLC, part of Springer Nature.
Συγγραφείς:
Khanna, N.N.
Jamthikar, A.D.
Gupta, D.
Piga, M.
Saba, L.
Carcassi, C.
Giannopoulos, A.A.
Nicolaides, A.
Laird, J.R.
Suri, H.S.
Mavrogeni, S.
Protogerou, A.D.
Sfikakis, P.
Kitas, G.D.
Suri, J.S.
Περιοδικό:
Current Atherosclerosis Reports
Εκδότης:
Current Medicine Group LLC 1
Λέξεις-κλειδιά:
arterial wall thickness; atherosclerosis; B scan; body mass; cardiovascular disease assessment; cardiovascular risk; carotid artery; carotid atherosclerosis; coronary artery calcification; deep learning; diabetes mellitus; disease association; disease exacerbation; human; hypertension; image analysis; insulin resistance; machine learning; pathogenesis; physical inactivity; prevalence; radiocardiography; Review; rheumatoid arthritis; risk assessment; smoking; atherosclerosis; atherosclerotic plaque; carotid artery disease; complication; diagnostic imaging; echography; inflammation; metabolism; optical coherence tomography; pathology; rheumatoid arthritis; risk factor, Arthritis, Rheumatoid; Atherosclerosis; Carotid Arteries; Carotid Artery Diseases; Deep Learning; Humans; Inflammation; Plaque, Atherosclerotic; Risk Assessment; Risk Factors; Tomography, Optical Coherence; Ultrasonography
DOI:
10.1007/s11883-019-0766-x
