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Artificial intelligence and the future of radiographic scoring in rheumatoid arthritis: a viewpoint
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...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9743640/ https://www.ncbi.nlm.nih.gov/pubmed/36510330 http://dx.doi.org/10.1186/s13075-022-02972-x |
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author | Bird, Alix Oakden-Rayner, Lauren McMaster, Christopher Smith, Luke A. Zeng, Minyan Wechalekar, Mihir D. Ray, Shonket Proudman, Susanna Palmer, Lyle J. |
author_facet | Bird, Alix Oakden-Rayner, Lauren McMaster, Christopher Smith, Luke A. Zeng, Minyan Wechalekar, Mihir D. Ray, Shonket Proudman, Susanna Palmer, Lyle J. |
author_sort | Bird, Alix |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9743640 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-97436402022-12-13 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. Arthritis Res Ther Review 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. BioMed Central 2022-12-12 2022 /pmc/articles/PMC9743640/ /pubmed/36510330 http://dx.doi.org/10.1186/s13075-022-02972-x Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Review Bird, Alix Oakden-Rayner, Lauren McMaster, Christopher Smith, Luke A. Zeng, Minyan Wechalekar, Mihir D. Ray, Shonket Proudman, Susanna Palmer, Lyle J. Artificial intelligence and the future of radiographic scoring in rheumatoid arthritis: a viewpoint |
title | Artificial intelligence and the future of radiographic scoring in rheumatoid arthritis: a viewpoint |
title_full | Artificial intelligence and the future of radiographic scoring in rheumatoid arthritis: a viewpoint |
title_fullStr | Artificial intelligence and the future of radiographic scoring in rheumatoid arthritis: a viewpoint |
title_full_unstemmed | Artificial intelligence and the future of radiographic scoring in rheumatoid arthritis: a viewpoint |
title_short | Artificial intelligence and the future of radiographic scoring in rheumatoid arthritis: a viewpoint |
title_sort | artificial intelligence and the future of radiographic scoring in rheumatoid arthritis: a viewpoint |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9743640/ https://www.ncbi.nlm.nih.gov/pubmed/36510330 http://dx.doi.org/10.1186/s13075-022-02972-x |
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