Anatomical variation of the sacroiliac joints: an MRI study with synthetic CT images
Bibliographic record
Abstract
BACKGROUND: Synthetic computed tomography (sCT) images are magnetic resonance imaging (MRI)-based images, generated using artificial intelligence. This study aimed to determine the prevalence of anatomical variants of sacroiliac joints (SIJ) on sCT images and the correlation with age, sex and body weight. METHODS: MRI of the SIJ including sCT images of 215 patients clinically suspected for sacroiliitis were retrospectively analyzed. The presence of anatomical variants of the SIJ was assessed. Age, sex and body mass index at the time of the MRI were recorded. RESULTS: SIJ variants were found in 82.8% (356/430) of the evaluated joints. The most frequent variants were iliosacral complex (27.7%), bipartite iliac bony plate (27.2%) and crescent iliac bony plate (27%). One new variant was identified, consisting of an accessory facet of the SIJ on the superior side. Overall, SIJ variants were slightly more frequent in women (85.8% vs. 77.8%), but iliosacral complex was significantly more frequent in men. Isolated synostosis was more prevalent with advancing age, in contrast to semicircular defect and unfused ossification center. The occurrence of iliosacral complex was associated with higher BMI, while crescent iliac bony plate occurred more in patients with lower BMI. CONCLUSION: Over 80% of patients in this study, who were all suspected of sacroiliitis, had at least one SIJ variant. These variants may actually represent subtypes of the normal SIJ. sCT enables detection of very small or subtle findings including SIJ variants.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".