One-stop automated diagnostic system for active sacroiliitis in three-dimensional magnetic resonance images using artificial intelligent models: a retrospective study

Background: Axial spondyloarthritis (axSpA) can potentially progress to ankylosing spondylitis, and diagnostic delay may lead to irreversible structural damage. Although sacroiliac joint magnetic resonance imaging (MRI) can detect bone marrow edema (BME) for early diagnosis, the complex etiologies and reliance on expert interpretation impede efficient identification in chronic low back pain populations. Therefore, we developed an artificial intelligence (AI) system that integrates MRI analysis and automates report generation to facilitate the detection of active sacroiliitis and BME quantification. Methods: This retrospective study analyzed 691 patients (540 with axSpA and 151 with non-spondyloarthritis) from the Chinese People’s Liberation Army General Hospital (2011–2023). Data were split into training and testing cohorts (4:1 ratio), with five-fold cross-validation applied to the training set. The system comprises four modules: (1) image preprocessing (intensity normalization), (2) coarse-to-fine 3D U-Net-based quadrant segmentation, (3) ResNet18-driven edema recognition (depth/intensity classification), and (4) diagnostic report generation using the QWEN large language model. Results: Among 691 patients (335 active sacroiliitis-positive and 356 negative), the AI system achieved Spondyloarthritis Research Consortium of Canada (SPARCC) scores of (15.12±10.73) vs. (0.88±1.13) in positive versus negative groups, respectively. The quadrant segmentation module achieved DICE similarity coefficients above 0.7 on the training, validation, and testing datasets. The edema inflammation, depth, and intensity classifiers exhibited good performance on the testing dataset, with balanced accuracies of 77.54%, 76.26%, and 80.91%, and area under the curve values of 0.85, 0.87, and 0.92, respectively. The intraclass correlation coefficient between SPARCC scores by our system and those by rheumatologists was 0.84 on the testing dataset. At the patient-level, our system achieved 90.81% sensitivity for diagnosis of active sacroiliitis on the testing dataset. Conclusion: The developed automated system enhances axSpA diagnostic efficiency by automating the identification of active sacroiliitis and enabling quantitative assessment of BME.