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

Alix Bird, Lauren Oakden-Rayner, Christopher McMaster, Luke A. Smith, Minyan Zeng, Mihir D. Wechalekar, Shonket Ray, Susanna Proudman, Lyle J. Palmer

Research output: Contribution to journalReview articlepeer-review

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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.

Original languageEnglish
Article number268
Number of pages10
JournalArthritis Research and Therapy
Volume24
Issue number1
DOIs
Publication statusPublished - Dec 2022

Keywords

  • Artificial intelligence
  • Deep learning
  • Radiographic scoring
  • Rheumatoid arthritis

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