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Review
. 2022 Dec 12;24(1):268.
doi: 10.1186/s13075-022-02972-x.

Artificial intelligence and the future of radiographic scoring in rheumatoid arthritis: a viewpoint

Alix Bird  1   2 , Lauren Oakden-Rayner  3   4 , Christopher McMaster  5 , Luke A Smith  3   4 , Minyan Zeng  3   4 , Mihir D Wechalekar  6 , Shonket Ray  7 , Susanna Proudman  8 , Lyle J Palmer  3   4
Affiliations
Review

Artificial intelligence and the future of radiographic scoring in rheumatoid arthritis: a viewpoint

Alix Bird et al. Arthritis Res Ther. .

Abstract

Rheumatoid arthritis is an autoimmune condition that predominantly affects the synovial joints, causing joint destruction, pain, and disability. Historically, the standard for measuring the long-term efficacy of disease-modifying antirheumatic drugs has been the assessment of plain radiographs with scoring techniques that quantify joint damage. However, with significant improvements in therapy, current radiographic scoring systems may no longer be fit for purpose for the milder spectrum of disease seen today. We argue that artificial intelligence is an apt solution to further improve upon radiographic scoring, as it can readily learn to recognize subtle patterns in imaging data to not only improve efficiency, but can also increase the sensitivity to variation in mild disease. Current work in the area demonstrates the feasibility of automating scoring but is yet to take full advantage of the strengths of artificial intelligence. By fully leveraging the power of artificial intelligence, faster and more sensitive scoring could enable the ongoing development of effective treatments for patients with rheumatoid arthritis.

Keywords: Artificial intelligence; Deep learning; Radiographic scoring; Rheumatoid arthritis.

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Conflict of interest statement

AB and LAS are funded by an unrestricted training grant from GlaxoSmithKline, supervised by LOR and LJP.

Figures

Fig. 1
Fig. 1
Output of neural network trained to detect joints in the hands
Fig. 2
Fig. 2
Flow diagram regarding study identification and selection [32]. *Reason 1: not investigating radiographic scoring; reason 2: not using machine learning; reason 3: using a different imaging modality; reason 4: lacked sufficient information for analysis
Fig. 3
Fig. 3
Using a convolutional neural network (CNN), joints are identified from the input radiograph and the score for each joint is assigned
See this image and copyright information in PMC

References

    1. Firestein GS. Evolving concepts of rheumatoid arthritis. Nature. 2003;423(6937):356–361. doi: 10.1038/nature01661. - DOI - PubMed
    1. Uhlig T, Moe RH, Kvien TK. The burden of disease in rheumatoid arthritis. Pharmacoeconomics. 2014;32(9):841–851. doi: 10.1007/s40273-014-0174-6. - DOI - PubMed
    1. Birnbaum H, Pike C, Kaufman R, Marynchenko M, Kidolezi Y, Cifaldi M. Societal cost of rheumatoid arthritis patients in the US. Curr Med Res Opin. 2010;26(1):77–90. doi: 10.1185/03007990903422307. - DOI - PubMed
    1. Aletaha D, Smolen JS. Diagnosis and management of rheumatoid arthritis: a review. JAMA. 2018;320(13):1360–1372. doi: 10.1001/jama.2018.13103. - DOI - PubMed
    1. Felson DT, Smolen JS, Wells G, Zhang B, van Tuyl LHD, Funovits J, et al. American College of Rheumatology/European League Against Rheumatism provisional definition of remission in rheumatoid arthritis for clinical trials. Arthritis Rheum. 2011;63(3):573–586. doi: 10.1002/art.30129. - DOI - PMC - PubMed

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