To distinguish flexible and rigid lumbar curve from MRI texture analysis in adolescent idiopathic scoliosis: A feasibility study
Bibliographic record
Abstract
BACKGROUND: Imaging in side bending, supine, traction, fulcrum, and push prone are examples of methods used to evaluate the curve reduction of scoliotic spine. However, being able to determine spine curve flexibility from MRI would eliminate the need of additional X-ray radiation related to radiograph acquisition in side-bending. PURPOSE/HYPOTHESIS: To find specific texture features of lumbar postural muscles on MRI that can distinguish flexible from rigid lumbar scoliotic curves. We hypothesized that the changes occurring in postural muscles with scoliosis can be seen with MRI. STUDY TYPE: Retrospective study case control. POPULATION: With Institutional Review Board approval and informed consent, 15 adolescents with idiopathic scoliosis and scheduled for surgery were involved. FIELD STRENGTH/SEQUENCE: -weighted MR images were performed on a 1.5T system using a spin echo sequence in the axial direction. ASSESSMENT: The spinal erector, quadratus lumborum and psoas major muscles were analyzed using textural features. STATISTICAL TESTS: Principal component analysis (PCA) and agglomerative hierarchical clustering (AHC) were used to classify the lumbar postural muscles and calculate performance metrics. The lumbar flexibility index, measured from suspension tests, was used as ground truth measurement. RESULTS: The five discriminant features (out of 34 tested features) obtained from PCA were able to keep over 90% of the variability of the dataset. The right and left spinal erector and the left psoas major had the highest performance metrics to classify the spinal curve flexibility, with an accuracy over 0.80, a sensitivity over 0.82, a specificity over 0.68, and a Matthews correlation coefficient over 0.57. DATA CONCLUSION: This study analyzed MRI using texture information of muscle to distinguish flexible from rigid scoliotic curves. Some postural muscle such as the spinal erector and the psoas major are more likely to reflect the curve flexibility of a scoliotic participant. LEVEL OF EVIDENCE: 2 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2017.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".