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Deep learning for detection of radiographic sacroiliitis: achieving expert-level performance

BACKGROUND: Radiographs of the sacroiliac joints are commonly used for the diagnosis and classification of axial spondyloarthritis. The aim of this study was to develop and validate an artificial neural network for the detection of definite radiographic sacroiliitis as a manifestation of axial spond...

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Autores principales: Bressem, Keno K., Vahldiek, Janis L., Adams, Lisa, Niehues, Stefan Markus, Haibel, Hildrun, Rodriguez, Valeria Rios, Torgutalp, Murat, Protopopov, Mikhail, Proft, Fabian, Rademacher, Judith, Sieper, Joachim, Rudwaleit, Martin, Hamm, Bernd, Makowski, Marcus R., Hermann, Kay-Geert, Poddubnyy, Denis
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8028815/
https://www.ncbi.nlm.nih.gov/pubmed/33832519
http://dx.doi.org/10.1186/s13075-021-02484-0
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author Bressem, Keno K.
Vahldiek, Janis L.
Adams, Lisa
Niehues, Stefan Markus
Haibel, Hildrun
Rodriguez, Valeria Rios
Torgutalp, Murat
Protopopov, Mikhail
Proft, Fabian
Rademacher, Judith
Sieper, Joachim
Rudwaleit, Martin
Hamm, Bernd
Makowski, Marcus R.
Hermann, Kay-Geert
Poddubnyy, Denis
author_facet Bressem, Keno K.
Vahldiek, Janis L.
Adams, Lisa
Niehues, Stefan Markus
Haibel, Hildrun
Rodriguez, Valeria Rios
Torgutalp, Murat
Protopopov, Mikhail
Proft, Fabian
Rademacher, Judith
Sieper, Joachim
Rudwaleit, Martin
Hamm, Bernd
Makowski, Marcus R.
Hermann, Kay-Geert
Poddubnyy, Denis
author_sort Bressem, Keno K.
collection PubMed
description BACKGROUND: Radiographs of the sacroiliac joints are commonly used for the diagnosis and classification of axial spondyloarthritis. The aim of this study was to develop and validate an artificial neural network for the detection of definite radiographic sacroiliitis as a manifestation of axial spondyloarthritis (axSpA). METHODS: Conventional radiographs of the sacroiliac joints obtained in two independent studies of patients with axSpA were used. The first cohort comprised 1553 radiographs and was split into training (n = 1324) and validation (n = 229) sets. The second cohort comprised 458 radiographs and was used as an independent test dataset. All radiographs were assessed in a central reading session, and the final decision on the presence or absence of definite radiographic sacroiliitis was used as a reference. The performance of the neural network was evaluated by calculating areas under the receiver operating characteristic curves (AUCs) as well as sensitivity and specificity. Cohen’s kappa and the absolute agreement were used to assess the agreement between the neural network and the human readers. RESULTS: The neural network achieved an excellent performance in the detection of definite radiographic sacroiliitis with an AUC of 0.97 and 0.94 for the validation and test datasets, respectively. Sensitivity and specificity for the cut-off weighting both measurements equally were 88% and 95% for the validation and 92% and 81% for the test set. The Cohen’s kappa between the neural network and the reference judgements were 0.79 and 0.72 for the validation and test sets with an absolute agreement of 90% and 88%, respectively. CONCLUSION: Deep artificial neural networks enable the accurate detection of definite radiographic sacroiliitis relevant for the diagnosis and classification of axSpA.
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spelling pubmed-80288152021-04-09 Deep learning for detection of radiographic sacroiliitis: achieving expert-level performance Bressem, Keno K. Vahldiek, Janis L. Adams, Lisa Niehues, Stefan Markus Haibel, Hildrun Rodriguez, Valeria Rios Torgutalp, Murat Protopopov, Mikhail Proft, Fabian Rademacher, Judith Sieper, Joachim Rudwaleit, Martin Hamm, Bernd Makowski, Marcus R. Hermann, Kay-Geert Poddubnyy, Denis Arthritis Res Ther Research Article BACKGROUND: Radiographs of the sacroiliac joints are commonly used for the diagnosis and classification of axial spondyloarthritis. The aim of this study was to develop and validate an artificial neural network for the detection of definite radiographic sacroiliitis as a manifestation of axial spondyloarthritis (axSpA). METHODS: Conventional radiographs of the sacroiliac joints obtained in two independent studies of patients with axSpA were used. The first cohort comprised 1553 radiographs and was split into training (n = 1324) and validation (n = 229) sets. The second cohort comprised 458 radiographs and was used as an independent test dataset. All radiographs were assessed in a central reading session, and the final decision on the presence or absence of definite radiographic sacroiliitis was used as a reference. The performance of the neural network was evaluated by calculating areas under the receiver operating characteristic curves (AUCs) as well as sensitivity and specificity. Cohen’s kappa and the absolute agreement were used to assess the agreement between the neural network and the human readers. RESULTS: The neural network achieved an excellent performance in the detection of definite radiographic sacroiliitis with an AUC of 0.97 and 0.94 for the validation and test datasets, respectively. Sensitivity and specificity for the cut-off weighting both measurements equally were 88% and 95% for the validation and 92% and 81% for the test set. The Cohen’s kappa between the neural network and the reference judgements were 0.79 and 0.72 for the validation and test sets with an absolute agreement of 90% and 88%, respectively. CONCLUSION: Deep artificial neural networks enable the accurate detection of definite radiographic sacroiliitis relevant for the diagnosis and classification of axSpA. BioMed Central 2021-04-08 2021 /pmc/articles/PMC8028815/ /pubmed/33832519 http://dx.doi.org/10.1186/s13075-021-02484-0 Text en © The Author(s) 2021 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 Research Article
Bressem, Keno K.
Vahldiek, Janis L.
Adams, Lisa
Niehues, Stefan Markus
Haibel, Hildrun
Rodriguez, Valeria Rios
Torgutalp, Murat
Protopopov, Mikhail
Proft, Fabian
Rademacher, Judith
Sieper, Joachim
Rudwaleit, Martin
Hamm, Bernd
Makowski, Marcus R.
Hermann, Kay-Geert
Poddubnyy, Denis
Deep learning for detection of radiographic sacroiliitis: achieving expert-level performance
title Deep learning for detection of radiographic sacroiliitis: achieving expert-level performance
title_full Deep learning for detection of radiographic sacroiliitis: achieving expert-level performance
title_fullStr Deep learning for detection of radiographic sacroiliitis: achieving expert-level performance
title_full_unstemmed Deep learning for detection of radiographic sacroiliitis: achieving expert-level performance
title_short Deep learning for detection of radiographic sacroiliitis: achieving expert-level performance
title_sort deep learning for detection of radiographic sacroiliitis: achieving expert-level performance
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8028815/
https://www.ncbi.nlm.nih.gov/pubmed/33832519
http://dx.doi.org/10.1186/s13075-021-02484-0
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