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

  • Evidence-Based Medicine, Clinical Trials and Their Interpretations (L. Roever, Section Editor)
  • Published:
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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.

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.

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Authors and Affiliations

  1. Department of Cardiology, Indraprastha Apollo Hospitals, New Delhi, India

    Narendra N. Khanna

  2. Department of Electronics and Communication Engineering, Visvesvaraya National Institute of Technology, Nagpur, Maharashtra, India

    Ankush D. Jamthikar & Deep Gupta

  3. Department of Rheumatology, University Clinic and AOU of Cagliari, Cagliari, Italy

    Matteo Piga

  4. Department of Radiology, University of Cagliari, Cagliari, Italy

    Luca Saba

  5. Department of Genetics, University of Cagliari, Cagliari, Italy

    Carlo Carcassi

  6. Department of Medicine, Imperial College London, London, UK

    Argiris A. Giannopoulos

  7. Department of Vascular Surgery, Imperial College London, London, UK

    Andrew Nicolaides

  8. Vascular Diagnostic Center, University of Cyprus, Nicosia, Cyprus

    Andrew Nicolaides

  9. Heart and Vascular Institute, Adventist Health St. Helena, St. Helena, CA, USA

    John R. Laird

  10. Brown University, Providence, RI, USA

    Harman S. Suri

  11. Cardiology Clinic, Onassis Cardiac Surgery Center, Athens, Greece

    Sophie Mavrogeni

  12. Cardiovascular Prevention Unit, Department of Pathophysiology, NKU of Athens, Athens, Greece

    A.D. Protogerou

  13. Rheumatology Unit, Laikon University Hospital, Athens, Greece

    Petros Sfikakis

  14. Medical School, National Kapodistrian University of Athens, Athens, Greece

    Petros Sfikakis

  15. Arthritis Research UK Centre for Epidemiology, Manchester University, Manchester, UK

    George D. Kitas

  16. Research and Development, Academic Affairs, Dudley Group NHS Foundation Trust, Dudley, UK

    George D. Kitas

  17. Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, USA

    Jasjit S. Suri

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  1. Narendra N. Khanna
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Correspondence to Jasjit S. Suri.

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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.

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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). https://doi.org/10.1007/s11883-019-0766-x

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