Please use this identifier to cite or link to this item: https://ahro.austin.org.au/austinjspui/handle/1/31839
Title: Artificial intelligence and the future of radiographic scoring in rheumatoid arthritis: a viewpoint.
Austin Authors: Bird, Alix;Oakden-Rayner, Lauren;McMaster, Christopher ;Smith, Luke A;Zeng, Minyan;Wechalekar, Mihir D;Ray, Shonket;Proudman, Susanna;Palmer, Lyle J
Affiliation: Australian Institute of Machine Learning, University of Adelaide, Corner Frome Road and North Terrace, Adelaide, SA.
Australian Institute of Machine Learning, University of Adelaide, Corner Frome Road and North Terrace, Adelaide, SA, 5000, Australia.
Rheumatology
Australian Institute of Machine Learning, University of Adelaide, Corner Frome Road and North Terrace, Adelaide, SA, 5000, Australia.
Department of Rheumatology, Flinders Medical Centre, and College of Medicine and Public Health, Flinders University, Bedford Park, SA, 5042, Australia.
Artificial Intelligence and Machine Learning, GlaxoSmithKline, South San Francisco, CA, USA.
Department of Rheumatology, Royal Adelaide Hospital, Adelaide, SA, 5000, Australia.
Issue Date: 12-Dec-2022
Date: 2022
Publication information: Arthritis Research &Therapy 2022; 24(1)
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.
URI: https://ahro.austin.org.au/austinjspui/handle/1/31839
DOI: 10.1186/s13075-022-02972-x
ORCID: 0000-0002-8676-0822
Journal: Arthritis Research &Therapy
Start page: 268
PubMed URL: 36510330
ISSN: 1478-6362
Type: Journal Article
Subjects: Artificial intelligence
Deep learning
Radiographic scoring
Rheumatoid arthritis
Arthritis, Rheumatoid/diagnostic imaging
Arthritis, Rheumatoid/drug therapy
Antirheumatic Agents/therapeutic use
Appears in Collections:Journal articles

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