ArticleRheumatology international2020
The semi-automated algorithm for the detection of bone marrow oedema lesions in patients with axial spondyloarthritis.
Article in Rheumatology international, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 20 citations in OpenAlex.
- Automated detection of spinal bone marrow oedema in axial spondyloarthritis: training and validation using two large phase 3 trial datasets.Rheumatology (Oxford, England) · 2025Trial
- Can radiomics replace the SPARCC scoring system in evaluating bone marrow edema of sacroiliac joints in patients with axial spondyloarthritis?Clinical rheumatology · 2023Trial
- Learning from multiple readings for axial spondyloarthritis classification of the sacroiliac joints.Scientific reports · 2026Article
- Deep learning detects retropharyngeal edema on MRI in patients with acute neck infections.European radiology experimental · 2025Article
- Performance of Fully Automated Algorithm Detecting Bone Marrow Edema in Sacroiliac Joints.Journal of clinical medicine · 2023Article
- Article
- Use of Imaging in Axial Spondyloarthritis for Diagnosis and Assessment of Disease Remission in the Year 2022.Current rheumatology reports · 2022Review
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The aim of the study was to create the efficient tool for semi-automated detection of bone marrow oedema lesions in patients with axial spondyloarthritis (axSpA). MRI examinations of 22 sacroiliac joints of patients with confirmed axSpA-related sacroiliitis (median SPARCC score: 14 points) were included into the study. Design of our algorithm is based on Maksymowych et al. evaluation method and consists of the following steps: manual segmentation of bones (T1W sequence), automated detection of reference signal region, sacroiliac joint central lines and ROIs, a division of ROIs into quadrants, automated detection of inflammatory changes (STIR sequence). As a gold standard, two sets of manual lesion delineations were created. Two approaches to the performance assessment of lesion detection were considered: pixel-wise (detections compared pixel by pixel) and quadrant-wise (quadrant to quadrant). Statistical analysis was performed using Spearman's correlation coefficient. Correlation coefficient obtained for pixel-wise comparison of semi-automated and manual detections was 0.87 (p = 0.001), while for quadrant-wise analysis was 0.83 (p = 0.001). The correlation between two sets of manual detections was 0.91 for pixel-wise comparison (p = 0.001) and 0.88 (p = 0.001) for quadrant-wise approach. Spearman's correlation between two manual assessments was not statistically different from the correlation between semi-automated and manual evaluations, both for pixel- (p = 0.14) and quadrant-wise (p = 0.17) analysis. Average single slice processing time: 0.64 ± 0.30 s. Our method allows for objective detection of bone marrow oedema lesions in patients with axSpA. The quantification of affected pixels and quadrants has comparable reliability to manual assessment.
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