Clinical workflow for spinal curvature measurement with portable ultrasound
Bibliographic record
Abstract
PURPOSE: Spinal curvature monitoring is essential in making treatment decisions in scoliosis. Monitoring entails radiographic examinations, however repeated ionizing radiation exposure has been shown to increase cancer risk. Ultrasound does not emit ionizing radiation and is safer for spinal curvature monitoring. We investigated a clinical sonography protocol and challenges associated with position-tracked ultrasound in spinal curvature measurement in scoliosis. METHODS: Transverse processes were landmarked along each vertebra using tracked ultrasound snapshots. The transverse process angle was used to determine the orientation of each vertebra. We tested our methodology on five patients in a local pediatric scoliosis clinic, comparing ultrasound to radiographic curvature measurements. RESULTS: Despite strong correlation between radiographic and ultrasound curvature angles in phantom studies, we encountered new challenges in the clinical setting. Our main challenge was differentiating transverse processes from ribs and other structures during landmarking. We observed up to 13° angle variability for a single vertebra and a 9.85° ± 10.81° difference between ultrasound and radiographic Cobb angles for thoracic curvatures. Additionally, we were unable to visualize anatomical landmarks in the lumbar region where soft tissue depth was 25–35mm. In volunteers with large Cobb angles (greater than 40° thoracic and 60° lumbar), we observed spinal protrusions resulting in incomplete probe-skin contact and partial ultrasound images not suitable for landmarking. CONCLUSION: Spinal curvature measurement using tracked ultrasound is viable on phantom spine models. In the clinic, new challenges were encountered which must be resolved before a universal sonography protocol can be developed.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.012 |
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".