Deep learning methods in the diagnosis of sacroiliitis from plain pelvic radiographs

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dc.authoridOrhan, Kevser/0000-0001-8639-751X
dc.contributor.authorUreten, Kemal
dc.contributor.authorMaras, Yuksel
dc.contributor.authorDuran, Semra
dc.contributor.authorGok, Kevser
dc.date.accessioned2025-01-21T16:37:16Z
dc.date.available2025-01-21T16:37:16Z
dc.date.issued2023
dc.departmentKırıkkale Üniversitesi
dc.description.abstractObjectives The aim of this study is to develop a computer-aided diagnosis method to assist physicians in evaluating sacroiliac radiographs. Methods Convolutional neural networks, a deep learning method, were used in this retrospective study. Transfer learning was implemented with pre-trained VGG-16, ResNet-101 and Inception-v3 networks. Normal pelvic radiographs (n = 290) and pelvic radiographs with sacroiliitis (n = 295) were used for the training of networks. Results The training results were evaluated with the criteria of accuracy, sensitivity, specificity and precision calculated from the confusion matrix and AUC (area under the ROC curve) calculated from ROC (receiver operating characteristic) curve. Pre-trained VGG-16 model revealed accuracy, sensitivity, specificity, precision and AUC figures of 89.9%, 90.9%, 88.9%, 88.9% and 0.96 with test images, respectively. These results were 84.3%, 91.9%, 78.8%, 75.6 and 0.92 with pre-trained ResNet-101, and 82.0%, 79.6%, 85.0%, 86.7% and 0.90 with pre-trained inception-v3, respectively. Conclusions Successful results were obtained with all three models in this study where transfer learning was applied with pre-trained VGG-16, ResNet-101 and Inception-v3 networks. This method can assist clinicians in the diagnosis of sacroiliitis, provide them with a second objective interpretation and also reduce the need for advanced imaging methods such as magnetic resonance imaging.
dc.identifier.doi10.1093/mr/roab124
dc.identifier.endpage206
dc.identifier.issn1439-7595
dc.identifier.issn1439-7609
dc.identifier.issue1
dc.identifier.pmid34888699
dc.identifier.scopus2-s2.0-85145491611
dc.identifier.scopusqualityQ2
dc.identifier.startpage202
dc.identifier.urihttps://doi.org/10.1093/mr/roab124
dc.identifier.urihttps://hdl.handle.net/20.500.12587/24443
dc.identifier.volume33
dc.identifier.wosWOS:000764786700001
dc.identifier.wosqualityQ4
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherOxford Univ Press
dc.relation.ispartofModern Rheumatology
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241229
dc.subjectSacroiliitis; deep learning; convolutional neural networks; transfer learning; pelvic plain radiographs
dc.titleDeep learning methods in the diagnosis of sacroiliitis from plain pelvic radiographs
dc.typeArticle

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