Recognition of vertebral compression fractures in magnetic resonance images using statistics of height and width
Bibliographic record
Abstract
Vertebral compression fractures (VCFs) present as partial collapses of vertebral bodies and may occur secondary to osteoporosis bone fragility and to metastatic cancer infiltration. The correct diagnosis of nontraumatic VCFs is therefore, fundamental for correct treatment. We aimed to classify VCFs using T1-weighted magnetic resonance images (MRI) of the lumbar spine acquired in the sagittal plane. Our study group comprised 63 patients (38 women and 25 men). From these patients 102 lumbar VCFs (53 benign and 49 malignant) and 89 normal vertebral bodies were manually segmented. The principal axis of each vertebral body region of interest was identified using moments. Statistical features of height and width measured perpendicular and parallel to the principal axis were computed. The k-nearest-neighbor method, a neural network with radial basis functions, and the naïve Bayes classifier were used with feature selection for classification. Areas under the receiver operating characteristic curve of 0.96 in the recognition of VCFs as compared with normal vertebral bodies and 0.73 for the classification of benign versus malignant VCFs were obtained. The proposed methods are promising for the recognition of VCFs, but additional features are needed to improve the classification of benign versus malignant VCFs.
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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 | 0.001 | 0.004 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".