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Artificial intelligence and the future of radiographic scoring in rheumatoid arthritis: a viewpoint.
Bird, Alix; Oakden-Rayner, Lauren; McMaster, Christopher; Smith, Luke A; Zeng, Minyan; Wechalekar, Mihir D; Ray, Shonket; Proudman, Susanna; Palmer, Lyle J.
Afiliación
  • Bird A; Australian Institute of Machine Learning, University of Adelaide, Corner Frome Road and North Terrace, Adelaide, SA, 5000, Australia. alix.bird@adelaide.edu.au.
  • Oakden-Rayner L; School of Public Health, The University of Adelaide, North Terrace, Adelaide, SA, 5000, Australia. alix.bird@adelaide.edu.au.
  • McMaster C; Australian Institute of Machine Learning, University of Adelaide, Corner Frome Road and North Terrace, Adelaide, SA, 5000, Australia.
  • Smith LA; School of Public Health, The University of Adelaide, North Terrace, Adelaide, SA, 5000, Australia.
  • Zeng M; Department of Rheumatology, Austin Health, Heidelberg, VIC, 3084, Australia.
  • Wechalekar MD; Australian Institute of Machine Learning, University of Adelaide, Corner Frome Road and North Terrace, Adelaide, SA, 5000, Australia.
  • Ray S; School of Public Health, The University of Adelaide, North Terrace, Adelaide, SA, 5000, Australia.
  • Proudman S; Australian Institute of Machine Learning, University of Adelaide, Corner Frome Road and North Terrace, Adelaide, SA, 5000, Australia.
  • Palmer LJ; School of Public Health, The University of Adelaide, North Terrace, Adelaide, SA, 5000, Australia.
Arthritis Res Ther ; 24(1): 268, 2022 12 12.
Article en En | MEDLINE | ID: mdl-36510330
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.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Artritis Reumatoide / Antirreumáticos Límite: Humans Idioma: En Revista: Arthritis Res Ther Asunto de la revista: REUMATOLOGIA Año: 2022 Tipo del documento: Article País de afiliación: Australia Pais de publicación: Reino Unido

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Artritis Reumatoide / Antirreumáticos Límite: Humans Idioma: En Revista: Arthritis Res Ther Asunto de la revista: REUMATOLOGIA Año: 2022 Tipo del documento: Article País de afiliación: Australia Pais de publicación: Reino Unido