Deep Learning on Spinal SPARCC Scoring System in Axial SpA Cohort
Bibliographic record
Abstract
Background: Deep learning models based on the Spondyloarthritis Research Consortium of Canada (SPARCC) scoring system have been used in previous studies to assess sacroiliac joint inflammation in patients with axial spondyloarthritis (SpA). However, these patients also commonly have active spinal inflammation, and detecting these changes holds diagnostic, prognostic, and therapeutic significance. This study aimed to develop a deep learning algorithm for spinal inflammation based on SPARCC scoring system in patients with axial SpA. Methods: The study cohort included 330 participants with axial SpA. All patients underwent whole spine magnetic resonance imaging (MRI) with short tau inversion recovery (STIR) sequence by 3T MR unit. Three independent readers identified regions of interest (ROIs) to identify bone marrow edema (BME) and performed SPARCC scoring. Two deep learning models based on attention Unet were trained. The BME model was employed to differentiate image with or without spinal inflammation and to delineate BMEs. The vertebral body-intervertebral disc model was utilized to identify discovertebral units. Setting the threshold brightness was applied to detect the region with CSF. The intraclass correlation coefficient (ICC) and Pearson coefficient were used to evaluate the agreement and the correlation between the score of human readers and the score of deep learning-based pipeline. Performance of the models was evaluated using sensitivity, specificity, accuracy, and the Dice coefficient. Results: The ICC and the Pearson coefficient between the SPARCC scores from three human readers and the deep learning-based scoring pipeline were 0.80 and 0.82, respectively. The sensitivity and specificity of identifying image with spinal inflammation were 0.90 and 0.84, respectively. The accuracy of identifying the region containing CSF was 0.88 in images with spinal inflammation. The Dice coefficients were 0.81 (vertebral bodies) and 0.80 (intervertebral disc) in images with spinal inflammation. Conclusion: The high consistency with human readers suggested that the deep learning-based pipeline could offer a SPARCC-informed approach for scoring spinal STIR images in axial SpA.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".