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


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.

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Papers of particular interest, published recently, have been highlighted as: • Of importance •• Of major importance

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Correspondence to Jasjit S. Suri.

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Narendra N. Khanna, Ankush D. Jamthikar, Deep Gupta, Matteo Piga, Luca Saba, Carlo Carcassi, Argiris A. Giannopoulos, Andrew Nicolaides, John R. Laird, Harman S. Suri, Sophie Mavrogeni, A.D. Protogerou, Petros Sfikakis, and George D. Kitas declare no conflict of interest. Jasjit S. Suri is affiliated to AtheroPoint™, focused in the area of stroke and cardiovascular imaging.

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Khanna, N.N., Jamthikar, A.D., Gupta, D. et al. Rheumatoid Arthritis: Atherosclerosis Imaging and Cardiovascular Risk Assessment Using Machine and Deep Learning–Based Tissue Characterization. Curr Atheroscler Rep 21, 7 (2019).

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  • Rheumatoid arthritis
  • Atherosclerosis
  • Cardiovascular risk assessment
  • Carotid ultrasound
  • Optical coherence tomography
  • Tissue characterization
  • Machine learning
  • Deep learning