Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Bird, Alix, Oakden-Rayner, Lauren, McMaster, Christopher, Smith, Luke A., Zeng, Minyan, Wechalekar, Mihir D., Ray, Shonket, Proudman, Susanna, Palmer, Lyle J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
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
_version_ 1784848765345071104
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
work_keys_str_mv AT birdalix artificialintelligenceandthefutureofradiographicscoringinrheumatoidarthritisaviewpoint
AT oakdenraynerlauren artificialintelligenceandthefutureofradiographicscoringinrheumatoidarthritisaviewpoint
AT mcmasterchristopher artificialintelligenceandthefutureofradiographicscoringinrheumatoidarthritisaviewpoint
AT smithlukea artificialintelligenceandthefutureofradiographicscoringinrheumatoidarthritisaviewpoint
AT zengminyan artificialintelligenceandthefutureofradiographicscoringinrheumatoidarthritisaviewpoint
AT wechalekarmihird artificialintelligenceandthefutureofradiographicscoringinrheumatoidarthritisaviewpoint
AT rayshonket artificialintelligenceandthefutureofradiographicscoringinrheumatoidarthritisaviewpoint
AT proudmansusanna artificialintelligenceandthefutureofradiographicscoringinrheumatoidarthritisaviewpoint
AT palmerlylej artificialintelligenceandthefutureofradiographicscoringinrheumatoidarthritisaviewpoint