Deep Learning Detects Changes Indicative of Axial Spondyloarthritis at MRI of Sacroiliac Joints
| العنوان: | Deep Learning Detects Changes Indicative of Axial Spondyloarthritis at MRI of Sacroiliac Joints |
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| المؤلفون: | Keno K. Bressem, Lisa C. Adams, Fabian Proft, Kay Geert A. Hermann, Torsten Diekhoff, Laura Spiller, Stefan M. Niehues, Marcus R. Makowski, Bernd Hamm, Mikhail Protopopov, Valeria Rios Rodriguez, Hildurn Haibel, Judith Rademacher, Murat Torgutalp, Robert G. Lambert, Xenofon Baraliakos, Walter P. Maksymowych, Janis L. Vahldiek, Denis Poddubnyy |
| المصدر: | Radiology. 305:655-665 |
| بيانات النشر: | Radiological Society of North America (RSNA), 2022. |
| سنة النشر: | 2022 |
| مصطلحات موضوعية: | Adult, Deep Learning, Spondylarthritis, Humans, Female, Sacroiliac Joint, Radiology, Nuclear Medicine and imaging, Magnetic Resonance Imaging, Axial Spondyloarthritis |
| الوصف: | Background MRI is frequently used for early diagnosis of axial spondyloarthritis (axSpA). However, evaluation is time-consuming and requires profound expertise because noninflammatory degenerative changes can mimic axSpA, and early signs may therefore be missed. Deep neural networks could function as assistance for axSpA detection. Purpose To create a deep neural network to detect MRI changes in sacroiliac joints indicative of axSpA. Materials and Methods This retrospective multicenter study included MRI examinations of five cohorts of patients with clinical suspicion of axSpA collected at university and community hospitals between January 2006 and September 2020. Data from four cohorts were used as the training set, and data from one cohort as the external test set. Each MRI examination in the training and test sets was scored by six and seven raters, respectively, for inflammatory changes (bone marrow edema, enthesitis) and structural changes (erosions, sclerosis). A deep learning tool to detect changes indicative of axSpA was developed. First, a neural network to homogenize the images, then a classification network were trained. Performance was evaluated with use of area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. |
| تدمد: | 1527-1315 0033-8419 |
| الوصول الحر: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::080ae9da682ecdc1572aa93d84897a66Test https://doi.org/10.1148/radiol.212526Test |
| رقم الانضمام: | edsair.doi.dedup.....080ae9da682ecdc1572aa93d84897a66 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 15271315 00338419 |
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