{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T01:19:53Z","timestamp":1788830393703,"version":"build-2803163510"},"reference-count":25,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2021,12,9]],"date-time":"2021-12-09T00:00:00Z","timestamp":1639008000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,1,3]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n               <jats:sec>\n                  <jats:title>Objectives<\/jats:title>\n                  <jats:p>The aim of this study is to develop a computer-aided diagnosis method to assist physicians in evaluating sacroiliac radiographs.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Methods<\/jats:title>\n                  <jats:p>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\u2009=\u2009290) and pelvic radiographs with sacroiliitis (n\u2009=\u2009295) were used for the training of networks.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>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.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Conclusions<\/jats:title>\n                  <jats:p>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.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/mr\/roab124","type":"journal-article","created":{"date-parts":[[2021,12,10]],"date-time":"2021-12-10T05:47:23Z","timestamp":1639115243000},"page":"202-206","source":"Crossref","is-referenced-by-count":25,"title":["Deep learning methods in the diagnosis of sacroiliitis from plain pelvic radiographs"],"prefix":"10.1093","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7673-4399","authenticated-orcid":false,"given":"Kemal","family":"\u00dcreten","sequence":"first","affiliation":[{"name":"Department of Rheumatology, Faculty of Medicine, K\u0131r\u0131kkale University , Ankara, Turkey"},{"name":"Computer Engineering Department, MSc, \u00c7ankaya University , Ankara, 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