Using ultrasound imaging to identify landmarks in vertebra models to assess spinal deformity
Bibliographic record
Abstract
Scoliosis is a type of spinal deformity that commonly develops in adolescents. Cobb angle, using the most tilted vertebrae, is the gold standard to assess scoliosis on radiographs. However, regularly taking radiographs introduces harmful ionizing radiation to patients, thus non-ionizing radiation methods have been explored for many years. Ultrasound has been proposed as one of the non-ionizing radiation methods to measure the deformity. This research was divided into two studies: 1) to investigate the reliability and repeatability of a new proposed method to measure Cobb angle; 2) to determine if landmarks can be identified from ultrasound images to measure curvature of spine. Based on the two studies, the feasibility of using ultrasound images to assess spinal deformity will be determined. Thirty-nine radiographs were used in the first study. The new method agreed well with the traditional Cobb method with intraclass correlation coefficient (ICC) value greater than 0.7 in different severity groups, and the average angle difference was 1.6° ± 3.1°. The second study showed laminae and transverse processes could be recognized from ultrasound images. The difference of the width of the laminae between the phantom and the ultrasound image was 0.3 mm. Therefore, it is feasible to use the proposed method and the laminae from the ultrasound images to assess the severity of 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 | 0.002 |
| 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".