dc.contributor.author
Bressem, Keno K.
dc.contributor.author
Vahldiek, Janis L.
dc.contributor.author
Adams, Lisa
dc.contributor.author
Niehues, Stefan Markus
dc.contributor.author
Haibel, Hildrun
dc.contributor.author
Rodriguez, Valeria Rios
dc.contributor.author
Torgutalp, Murat
dc.contributor.author
Protopopov, Mikhail
dc.contributor.author
Proft, Fabian
dc.contributor.author
Rademacher, Judith
dc.contributor.author
Sieper, Joachim
dc.contributor.author
Rudwaleit, Martin
dc.contributor.author
Hamm, Bernd
dc.contributor.author
Makowski, Marcus R.
dc.contributor.author
Hermann, Kay-Geert
dc.contributor.author
Poddubnyy, Denis
dc.date.accessioned
2023-03-17T15:31:41Z
dc.date.available
2023-03-17T15:31:41Z
dc.identifier.uri
https://refubium.fu-berlin.de/handle/fub188/38451
dc.identifier.uri
http://dx.doi.org/10.17169/refubium-38169
dc.description.abstract
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.
en
dc.rights.uri
https://creativecommons.org/licenses/by/4.0/
dc.subject
Axial spondyloarthritis
en
dc.subject
Sacroiliitis
en
dc.subject
Artificial intelligence
en
dc.subject
Deep learning
en
dc.subject
Machine learning
en
dc.subject.ddc
600 Technik, Medizin, angewandte Wissenschaften::610 Medizin und Gesundheit::610 Medizin und Gesundheit
dc.title
Deep learning for detection of radiographic sacroiliitis: achieving expert-level performance
dc.type
Wissenschaftlicher Artikel
dcterms.bibliographicCitation.articlenumber
106
dcterms.bibliographicCitation.doi
10.1186/s13075-021-02484-0
dcterms.bibliographicCitation.journaltitle
Arthritis Research & Therapy
dcterms.bibliographicCitation.originalpublishername
Springer Nature
dcterms.bibliographicCitation.volume
23
refubium.affiliation
Charité - Universitätsmedizin Berlin
refubium.funding
Springer Nature DEAL
refubium.resourceType.isindependentpub
no
dcterms.accessRights.openaire
open access
dcterms.bibliographicCitation.pmid
33832519
dcterms.isPartOf.eissn
1478-6362