{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,12]],"date-time":"2026-09-12T19:28:57Z","timestamp":1789241337826,"version":"build-2803163510"},"reference-count":38,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2023,12,12]],"date-time":"2023-12-12T00:00:00Z","timestamp":1702339200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Purpose: Sacroiliitis refers to the inflammatory condition of the sacroiliac joints, frequently causing lower back pain. It is often associated with systemic conditions. However, its signs on radiographic images can be subtle, which may result in it being overlooked or underdiagnosed. This study aims to utilize artificial intelligence (AI) to create a diagnostic tool for more accurate sacroiliitis detection in radiological images, with the goal of optimizing treatment plans and improving patient outcomes. Materials and Method: The study included 492 patients who visited our hospital. Right sacroiliac joint films were independently evaluated by two musculoskeletal radiologists using the Modified New York criteria (Normal, Grades 1\u20134). A consensus reading resolved disagreements. The images were preprocessed with Z-score standardization and histogram equalization. The DenseNet121 algorithm, a convolutional neural network with 201 layers, was used for learning and classification. All steps were performed on the DEEP:PHI platform. Result: The AI model exhibited high accuracy across different grades: 94.53% (Grade 1), 95.83% (Grade 2), 98.44% (Grade 3), 96.88% (Grade 4), and 96.09% (Normal cases). Sensitivity peaked at Grade 3 and Normal cases (100%), while Grade 4 achieved perfect specificity (100%). PPVs ranged from 82.61% (Grade 1) to 100% (Grade 4), and NPVs peaked at 100% for Grade 3 and Normal cases. The F1 scores ranged from 64.41% (Grade 1) to 95.38% (Grade 3). Conclusions: The AI diagnostic model showcased a robust performance in detecting and grading sacroiliitis, reflecting its potential to enhance diagnostic accuracy in clinical settings. By facilitating earlier and more accurate diagnoses, this model could substantially impact treatment strategies and patient outcomes.<\/jats:p>","DOI":"10.3390\/diagnostics13243643","type":"journal-article","created":{"date-parts":[[2023,12,12]],"date-time":"2023-12-12T04:28:31Z","timestamp":1702355311000},"page":"3643","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["The Development and Validation of an AI Diagnostic Model for Sacroiliitis: A Deep-Learning Approach"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1069-0810","authenticated-orcid":false,"given":"Kyu-Hong","family":"Lee","sequence":"first","affiliation":[{"name":"Department of Radiology, College of Medicine, Inha University, Incheon 22212, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8678-8059","authenticated-orcid":false,"given":"Ro-Woon","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Radiology, College of Medicine, Inha University, Incheon 22212, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kyung-Hee","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Radiology, College of Medicine, Inha University, Incheon 22212, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0004-8034","authenticated-orcid":false,"given":"Won","family":"Park","sequence":"additional","affiliation":[{"name":"Division of Rheumatology, Department of Internal Medicine, College of Medicine, Inha University, Incheon 22212, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1262-2790","authenticated-orcid":false,"given":"Seong-Ryul","family":"Kwon","sequence":"additional","affiliation":[{"name":"Division of Rheumatology, Department of Internal Medicine, College of Medicine, Inha University, Incheon 22212, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mie-Jin","family":"Lim","sequence":"additional","affiliation":[{"name":"Division of Rheumatology, Department of Internal Medicine, College of Medicine, Inha University, Incheon 22212, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,12,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1136\/ard.2010.133645","article-title":"The Assessment of SpondyloArthritis International Society classification criteria for peripheral spondyloarthritis and for spondyloarthritis in general","volume":"70","author":"Rudwaleit","year":"2011","journal-title":"Ann. Rheum. Dis."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1002\/art.1780270401","article-title":"Evaluation of diagnostic criteria for ankylosing spondylitis. A proposal for modification of the New York criteria","volume":"27","author":"Valkenburg","year":"1984","journal-title":"Arthritis Rheum."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1998","DOI":"10.1136\/annrheumdis-2012-201945","article-title":"The frequency of non-radiographic axial spondyloarthritis in relation to symptom duration in patients referred because of chronic back pain: Results from the Berlin Early Spondyloarthritis Clinic","volume":"71","author":"Poddubnyy","year":"2012","journal-title":"Ann. Rheum. Dis."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1148\/radiology.151.2.6709915","article-title":"The normal sacroiliac joint: A CT study of asymptomatic patients","volume":"151","author":"Vogler","year":"1984","journal-title":"Radiology"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1252","DOI":"10.1177\/0284185116688377","article-title":"Radiography and abdominal CT compared with sacroiliac joint CT in the diagnosis of sacroiliitis","volume":"58","author":"Melchior","year":"2017","journal-title":"Acta Radiol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1056\/NEJMra072149","article-title":"Computed tomography\u2014An increasing source of radiation exposure","volume":"357","author":"Brenner","year":"2007","journal-title":"N. Engl. J. Med."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1958","DOI":"10.1136\/annrheumdis-2015-208642","article-title":"Defining active sacroiliitis on MRI for classification of axial spondyloarthritis: Update by the ASAS MRI working group","volume":"75","author":"Lambert","year":"2016","journal-title":"Ann. Rheum. Dis."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"377","DOI":"10.1097\/BOR.0000000000000071","article-title":"Similarities and differences between nonradiographic and radiographic axial spondyloarthritis: A clinical, epidemiological, and therapeutic assessment","volume":"26","author":"Poddubnyy","year":"2014","journal-title":"Curr. Opin. Rheumatol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"663","DOI":"10.1016\/j.berh.2014.10.007","article-title":"The concept of Spondyloarthritis: Where are we now?","volume":"28","author":"Garg","year":"2014","journal-title":"Best Pract. Res. Clin. Rheumatol."},{"key":"ref_10","first-page":"S23","article-title":"Socioeconomic consequences of ankylosing spondylitis","volume":"20","author":"Boonen","year":"2002","journal-title":"Clin. Exp. Rheumatol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"262","DOI":"10.1038\/nrrheum.2012.39","article-title":"Referral strategies for early diagnosis of axial spondyloarthritis","volume":"8","author":"Rudwaleit","year":"2012","journal-title":"Nat. Rev. Rheumatol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2403","DOI":"10.1002\/art.38738","article-title":"Agreement between clinical practice and trained central reading in the reading of sacroiliac joints on plain pelvic radiographs. Results from the DESIR cohort","volume":"66","author":"Lenczner","year":"2014","journal-title":"Arthritis Rheumatol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"11591","DOI":"10.1073\/pnas.1806905115","article-title":"Deep neural network improves fracture detection by clinicians","volume":"115","author":"Lindsey","year":"2018","journal-title":"Proc. Nat. Acad. Sci. USA"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1038\/s41591-018-0300-7","article-title":"High-performance medicine: The convergence of human and artificial intelligence","volume":"25","author":"Topol","year":"2019","journal-title":"Nat. Med."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1186\/s13075-021-02484-0","article-title":"Deep learning for detection of radiographic sacroiliitis: Achieving expert-level performance","volume":"23","author":"Bressem","year":"2021","journal-title":"Arthritis Res. Ther."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1038\/s41586-019-1799-6","article-title":"International evaluation of an Ai system for breast cancer screening","volume":"577","author":"McKinney","year":"2020","journal-title":"Nature"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1038\/nature21056","article-title":"Dermatologist-level classification of skin cancer with deep neural networks","volume":"542","author":"Esteva","year":"2017","journal-title":"Nature"},{"key":"ref_18","unstructured":"Irvin, J., Rajpurkar, P., Ko, M., Yu, Y., Ciurea-Ilcus, S., Chute, C., Marklund, H., Haghgoo, B., Ball, R., and Shpanskaya, K. (February, January 27). Chexpert: A large chest radiograph dataset with uncertainty labels and expert comparison. Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence\/Thirty-First Innovative Applications of Artificial Intelligence Conference\/Ninth AAAI Symposium on Educational Advances in Artificial Intelligence, Honolulu, HI, USA."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1369","DOI":"10.1007\/s00256-020-03433-9","article-title":"Detection of hip osteoarthritis by using plain pelvic radiographs with deep learning methods","volume":"49","author":"Arslan","year":"2020","journal-title":"Skelet. Radiol."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., and Batra, D. (2017, January 22\u201329). Grad-cam: Visual explanations from deep networks via gradient-based localization. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.74"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"791","DOI":"10.1136\/annrheumdis-2015-208852","article-title":"The term \u201cnon-radiographic axial spondyloarthritis\u201d is much more important to classify than to diagnose patient with axial spondyloarthritis","volume":"75","author":"Deodhar","year":"2016","journal-title":"Ann. Rheum. Dis."},{"key":"ref_22","first-page":"125","article-title":"Radiological scoring methods in ankylosing spondylitis. Reliability and change over 1 and 2 years","volume":"31","author":"Spoorenberg","year":"2004","journal-title":"J. Rheumatol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"505","DOI":"10.1148\/rg.2017160130","article-title":"Machine learning for medical imaging","volume":"37","author":"Erickson","year":"2017","journal-title":"Radiographics"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2113","DOI":"10.1148\/rg.2017170077","article-title":"Deep learning: A primer for radiologists","volume":"37","author":"Chartrand","year":"2017","journal-title":"Radiographics"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Nasr-Esfahani, E., Samavi, S., Karimi, N., Soroushmehr, S.M.R., Jafari, M.H., Ward, K., and Najarian, K. (2016, January 16\u201320). Melanoma detection by analysis of clinical images using convolutional neural network. Proceedings of the 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Orlando, FL, USA.","DOI":"10.1109\/EMBC.2016.7590963"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1113","DOI":"10.21037\/qims.2018.12.04","article-title":"Discrimination of smoking status by MRI based on deep learning method","volume":"8","author":"Wang","year":"2018","journal-title":"Quant. Imaging Med. Surg."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1109\/TITB.2012.2227493","article-title":"Classification of color images of dermatological ulcers","volume":"17","author":"Pereira","year":"2013","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1007\/s11548-008-0154-4","article-title":"Reducing the semantic gap in content-based image retrieval in mammography with relevance feedback and inclusion of expert knowledge","volume":"3","author":"Rosa","year":"2008","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.cmpb.2018.02.015","article-title":"Radiomics-based features for pattern recognition of lung cancer histopathology and metastases","volume":"159","author":"Cipriano","year":"2018","journal-title":"Comput. Methods Prog. Biomed."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"272","DOI":"10.1055\/s-0039-1681086","article-title":"Analysis of machine learning algorithms for diagnosis of diffuse lung diseases","volume":"57","author":"Rangayyan","year":"2018","journal-title":"Methods Inf. Med."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Azevedo-Marques, P.M., Spagnoli, H.F., Frighetto-Pereira, L., Reis, R.M., Metzner, G.A., Rangayyan, R.M., and Nogueira-Barbosa, M.H. (2015, January 25\u201329). Classification of vertebral compression fractures in magnetic resonance images using spectral and fractal analysis. Proceedings of the 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Milan, Italy.","DOI":"10.1109\/EMBC.2015.7318464"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1971","DOI":"10.1007\/s11548-017-1625-2","article-title":"Cooperative strategy for a dynamic ensemble of classification models in clinical applications: The case of MRI vertebral compression fractures","volume":"12","author":"Casti","year":"2017","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Lee, S.-Y., Kang, H., Jeong, J.-H., and Kang, D.-Y. (2021). Performance evaluation in [18F]Florbetaben brain PET images classification using 3D Convolutional Neural Network. PLoS ONE, 16.","DOI":"10.1371\/journal.pone.0258214"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"e180031","DOI":"10.1148\/ryai.2019180031","article-title":"Challenges Related to Artificial Intelligence Research in Medical Imaging and the Importance of Image Analysis Competitions","volume":"1","author":"Prevedello","year":"2019","journal-title":"Radiol. Artif. Intell."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"519","DOI":"10.1136\/ard.62.6.519","article-title":"Radiographic assessment of sacroiliitis by radiologists and rheumatologists: Does training improve quality?","volume":"62","author":"Schulpen","year":"2003","journal-title":"Ann. Rheum. Dis."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"70","DOI":"10.3899\/jrheum.160079","article-title":"Limited reliability of radiographic assessment of sacroiliac joints in patients with suspected early spondyloarthritis","volume":"44","author":"Christiansen","year":"2017","journal-title":"J. Rheumatol."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Lee, K.H., Choi, S.T., Lee, G.Y., Ha, Y.J., and Choi, S.-I. (2021). Method for Diagnosing the Bone Marrow Edema of Sacroiliac Joint in Patients with Axial Spondyloarthritis Using Magnetic Resonance Image Analysis Based on Deep Learning. Diagnostics, 11.","DOI":"10.3390\/diagnostics11071156"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"7960","DOI":"10.1007\/s00330-021-07879-w","article-title":"Radiologists in the loop: The roles of radiologists in the development of AI applications","volume":"31","author":"Scheek","year":"2021","journal-title":"Eur. Radiol."}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/13\/24\/3643\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:37:09Z","timestamp":1760132229000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/13\/24\/3643"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,12]]},"references-count":38,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2023,12]]}},"alternative-id":["diagnostics13243643"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics13243643","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,12]]}}}