Classification of radiographic and non-radiographic axial spondylarthritis in pelvic radiography using deep convolution neural network models

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dc.authoridKahveci, Abdulvahap/0000-0002-2611-2372
dc.authoridAlcan, Veysel/0000-0002-7786-8591
dc.authoridataman, sebnem/0000-0003-3570-3825
dc.authoridUCAR, Murat/0000-0001-9997-4267
dc.authoridGumustepe, Alper/0000-0003-1585-2540
dc.contributor.authorKahveci, Abdulvahap
dc.contributor.authorAlcan, Veysel
dc.contributor.authorUcar, Murat
dc.contributor.authorGumustepe, Alper
dc.contributor.authorBilgin, Esra
dc.contributor.authorSunar, Ismihan
dc.contributor.authorAtaman, Sebnem
dc.date.accessioned2025-03-17T12:27:37Z
dc.date.available2025-03-17T12:27:37Z
dc.date.issued2025
dc.departmentTarsus Üniversitesi
dc.description.abstractDiscriminating radiographic axial spondyloarthritis (r-axSpA) from nonradiographic axial spondyloarthritis (nr-axSpA) using pelvic radiographs is challenging, especially for inexperienced clinicians. This study aims to perform deep convolution neuronal network (CNN) models to aid in this diagnostic challenge by using their radiographs. Six-hundred sacroiliac joint exams (300 pelvic radiographs) of patients from axSpA cohort were enrolled (screened between Jan 2010 and Jan 2020). All radiographs were examined and graded by a blinded expert rheumatologist. Four CNN models (VGG16, ResNet, DenseNet, and MobileNet) were proposed by combining them with the YOLOv7 object detection algorithm to mark the sacroiliac joints. The classification results of CNNs were evaluated by performance metrics [accuracy, AUROC (area under the receiver operating characteristic curve)]. The VGG16 model with the YOLOv7 algorithm yielded the best performance [accuracy of 83.8% (95% CI; 73.3-92.9%)]. The accuracy values of other models were 70.7% (58.3-82.7%), 77.1% (65.1-87.3%), and 71.8% (59.0-83.1%) for ResNet, DenseNet, and MobileNet, respectively. In the ROC analysis, the AUC value of the VGG16 model (AUC = 0.882) was higher than other CNNs (AUCs = 0.836, 0.808, and 0.787; DenseNet, ResNet, and MobileNet, respectively). This paper revealed deep learning architectures were able to differentiate r-axSpA from nr-axSpA on pelvic radiographs. Hereby, these models might be used as a clinical decision support system in clinical practice.
dc.identifier.doi10.1007/s10586-024-04920-7
dc.identifier.issn1386-7857
dc.identifier.issn1573-7543
dc.identifier.issue3
dc.identifier.scopus2-s2.0-85217275162
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1007/s10586-024-04920-7
dc.identifier.urihttps://hdl.handle.net/20.500.13099/2357
dc.identifier.volume28
dc.identifier.wosWOS:001401573600002
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSpringer
dc.relation.ispartofCluster Computing-The Journal of Networks Software Tools and Applications
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WOS_20250316
dc.subjectAxial spondyloarthritis
dc.subjectMachine learning
dc.subjectArtificial intelligence
dc.subjectDeep learning
dc.subjectPelvic radiography
dc.subjectSacroiliac joints
dc.titleClassification of radiographic and non-radiographic axial spondylarthritis in pelvic radiography using deep convolution neural network models
dc.typeArticle

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