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

  • 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
  • George D. Kitas
  • Jasjit S. SuriEmail author
Evidence-Based Medicine, Clinical Trials and Their Interpretations (L. Roever, Section Editor)
Part of the following topical collections:
  1. Topical Collection on Evidence-Based Medicine, Clinical Trials and Their Interpretations


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.


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


Compliance with Ethical Standards

Conflict of Interest

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.

Human and Animal Rights and Informed Consent

This article does not contain any studies with human or animal subjects performed by any of the authors.


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

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Narendra N. Khanna
    • 1
  • Ankush D. Jamthikar
    • 2
  • Deep Gupta
    • 2
  • Matteo Piga
    • 3
  • Luca Saba
    • 4
  • Carlo Carcassi
    • 5
  • Argiris A. Giannopoulos
    • 6
  • Andrew Nicolaides
    • 7
    • 8
  • John R. Laird
    • 9
  • Harman S. Suri
    • 10
  • Sophie Mavrogeni
    • 11
  • A.D. Protogerou
    • 12
  • Petros Sfikakis
    • 13
    • 14
  • George D. Kitas
    • 15
    • 16
  • Jasjit S. Suri
    • 17
    Email author
  1. 1.Department of CardiologyIndraprastha Apollo HospitalsNew DelhiIndia
  2. 2.Department of Electronics and Communication EngineeringVisvesvaraya National Institute of TechnologyNagpurIndia
  3. 3.Department of RheumatologyUniversity Clinic and AOU of CagliariCagliariItaly
  4. 4.Department of RadiologyUniversity of CagliariCagliariItaly
  5. 5.Department of GeneticsUniversity of CagliariCagliariItaly
  6. 6.Department of MedicineImperial College LondonLondonUK
  7. 7.Department of Vascular SurgeryImperial College LondonLondonUK
  8. 8.Vascular Diagnostic CenterUniversity of CyprusNicosiaCyprus
  9. 9.Heart and Vascular InstituteAdventist Health St. HelenaSt. HelenaUSA
  10. 10.Brown UniversityProvidenceUSA
  11. 11.Cardiology ClinicOnassis Cardiac Surgery CenterAthensGreece
  12. 12.Cardiovascular Prevention Unit, Department of PathophysiologyNKU of AthensAthensGreece
  13. 13.Rheumatology UnitLaikon University HospitalAthensGreece
  14. 14.Medical SchoolNational Kapodistrian University of AthensAthensGreece
  15. 15.Arthritis Research UK Centre for EpidemiologyManchester UniversityManchesterUK
  16. 16.Research and DevelopmentAcademic Affairs, Dudley Group NHS Foundation TrustDudleyUK
  17. 17.Stroke Monitoring and Diagnostic DivisionAtheroPoint™RosevilleUSA

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