ArticleMagma (New York, N.Y.)2025
Clinical validation of fully automated cartilage transverse relaxation time (T2) and thickness analysis using quantitative DESS magnetic resonance imaging.
Article in Magma (New York, N.Y.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Advances in cartilage imaging techniques.Nature reviews. Rheumatology · 2026Review
- Exploratory analysis of the 2-year changes in knee cartilage thickness and transverse relaxation time (T2) in ACL-injured versus healthy participants.Osteoarthritis and cartilage open · 2026Article
- The role of radiography and MRI as screening tools and outcome measures in clinical trials of knee osteoarthritis: a perspective on the current status and outlook.Therapeutic advances in musculoskeletal disease · 2026Review
Corrections and comments
- Erratum issued
Authors and funding
7 authors.
Funding
Abstract
objectiveTo clinically validate a fully automated cartilage segmentation technique from quantitative double-echo steady-state (qDESS) MRI supporting simultaneous estimation of cartilage T2 and morphology. Here, we test whether laminar (superficial and deep layer) T2 results from convolutional neural network (CNN) segmentations are consistent with those from manual expert segmentations. MATERIALS AND
methodsThe 3D qDESS sequence was acquired using 3 T MRI (resolution: 0.3125 × 0.3125x1.5 mm) in both knees of 37 subjects with unilateral anterior cruciate ligament (ACL) injury and 48 uninjured controls. Automated femorotibial cartilage (FTJ) segmentation was based on a 2D U-Net. Laminar T2 and cartilage thickness across the FTJ) were compared between ACL-injured and contralateral knees, and between ACL-injured and control knees. Effect sizes of these differences were measured using non-parametric Cohen's d (d
resultSignificant differences were observed only in deep T2, with longer T2 in ACL-injured knees than in contralateral and healthy control knees in most of the comparisons and with similar effect sizes for automated and manual segmentations (range d DISCUSSION: Fully-automated, CNN-based analysis showed similar sensitivity to differences in laminar cartilage T2 as manual segmentation, allowing automated qDESS-analyses to be applied to larger datasets.
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Registered trials
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