Ultrasound Imaging of Spinal Vertebrae to Study Scoliosis
Bibliographic record
Abstract
Scoliosis is a musculoskeletal disorder manifested as a three-dimensional spinal deformity. It affects 2% - 3% of the adolescent population. The conventional method to diagnose scoliosis is to measure the Cobb angle from posteroante-rior radiograph. Since radiation exposure is not desirable for patients, other non-ionizing methods have been explored. Among all the non-ionization methods, ultrasound (US) is a potential cost-effective method. However, our understand-ing of US interaction with the vertebrae or the spine is limited. The purpose of this study was to demonstrate the ability of US to identify bony landmarks for measuring spinal deformity. This study used a phased array US system with a 5 MHz transducer and a position encoder. In-vitro experiment on a cadaver vertebra and a pilot clinical study were carried out. The in-vitro experimental results showed that the lamina and spinous process were strong reflectors from the single vertebra. Less than 4% of error occurred on the dimension measurements. The pilot study was performed on a healthy subject and a scoliotic patient. The results indicated the lamina and spinous process could be identified and the curvature of the spine could be estimated using the reflectors. The difference of the curvature angle of the spine measured from the radiograph and the US images was 2?. These results have illustrated that US is a promising tool to measure curvature of spinal deformity and study scoliosis.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".