ArticleInsights into imaging2025
Enhanced diagnosis of axial spondyloarthritis using machine learning with sacroiliac joint MRI: a multicenter study.
Article in Insights into imaging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Diagnostic and prognostic performance of artificial intelligence and radiomics in ankylosing spondylitis: a systematic review and meta-analysis.Journal of robotic surgery · 2026Pooled it
- Multi-habitat radiomics on T2-FS MRI for identifying spatial patterns of axial spondyloarthritis-associated bone marrow edema.Clinical rheumatology · 2026Article
- Real-world comparative evaluation of quality of life and economic burden in patients with psoriatic arthritis and axial spondyloarthritis under different treatment strategies: a multicenter cross-sectional study.Health and quality of life outcomes · 2026Article
- A Comprehensive Clinical Decision Support System for the Early Diagnosis of Axial Spondyloarthritis: Multi-Sequence MRI, Clinical Risk Integration, and Explainable Segmentation.Diagnostics (Basel, Switzerland) · 2026Article
- Year in Review: Axial Spondyloarthritis.Mediterranean journal of rheumatology · 2026Review
- Anatomical Features of the Sacroiliac Joint and Machine Learning-Based Classification of Disease Types.Diagnostics (Basel, Switzerland) · 2026Article
- Biomarkers in axial spondyloarthritis diagnosis: from clinical signs to multi-omics integration.Frontiers in immunology · 2026Review
- Imaging pathways in spondyloarthritis: integrating radiography, ultrasonography, magnetic resonance imaging, low-dose computed tomography, and artificial intelligence methods : Radiology and AI in SpA.Rheumatology international · 2025Review
Corrections and comments
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Authors and funding
13 authors.
Funding
Abstract
objectivesTo develop a machine learning (ML)-based model using MRI and clinical risk factors to enhance diagnostic accuracy for axial spondyloarthritis (axSpA).
methodsWe retrospectively analyzed datasets from four centers (A-D), focusing on patients with chronic low back pain. A subset from center A was used for prospective validation. A deep learning (DL) model based on ResNet50 was constructed using sacroiliac joint MRI. Clinical variables were integrated with DL scores in ML algorithms to distinguish axSpA from non-axSpA patients. Model performance was assessed by the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy.
resultsThe study included 1294 patients (median age 31 years [interquartile range 24-42]; 35.5% females). Clinical risk factors identified were age, sex, and human leukocyte antigen-B27 status. The MRI-based DL model demonstrated an AUC of 0.837, 0.636, 0.724, 0.710, and 0.812 on the internal test set, three external test sets, and the prospective validation set, respectively. The combined model, particularly the K-nearest-neighbors-11 algorithm, demonstrated superior performance across multiple test sets with AUCs ranging from 0.853 to 0.912. It surpassed the Assessment of SpondyloArthritis International Society criteria with better AUC (0.858 vs. 0.650, p < 0.001), sensitivity (87.8% vs. 42.4%, p < 0.001), and accuracy (78.7% vs. 56.9%, p < 0.001).
conclusionThe ML method integrating MRI and clinical risk factors effectively identified axSpA, representing a promising tool for the diagnosis and management of axSpA. CLINICAL RELEVANCE STATEMENT: The machine learning model combining MRI and clinical risk factors potentially enables earlier diagnosis and intervention for axial spondyloarthritis patients, reducing the delays commonly associated with traditional diagnostic approaches. KEY POINTS: Axial spondyloarthritis (AxSpA) lacks definitive diagnostic criteria or markers, leading to diagnostic delay. MRI-based deep learning provided quantitative analysis of sacroiliac joint changes indicative of axSpA. A machine learning model combining sacroiliac joint MRI and clinical risk factors enhanced axSpA identification.
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