Improving Interpretability of 2-D Ultrasound of the Lumbar Spine
Bibliographic record
Abstract
Ultrasound-guided anesthesia uses a safe, portable imaging modality to provide visual feedback during the needle injection. Widespread adoption of ultrasound-guided anesthesia has been primarily limited by a lack of access to advanced ultrasound technology and a lack of ultrasound training for anesthesiologists. We sought to address these limitations by introducing a method that aids the interpretability of cross-sectional ultrasound from conventional (2D) machines. We propose a constrained registration of a 3D active shape model constructed from computerized tomography (CT) scans of the lumbar spine to a specific set of targets automatically extracted from 2D B-mode ultrasound images with machine learning models. The registration results in an overlay of the entire bone cross-section of the lumbar spine onto the ultrasound image. Our proposed registration achieved a mean squared error of 1.4 ±0.3 mm on a set of 43 ultrasound images, which is smaller than the key anatomical features, suggesting that the overlay is suitable for interpretation.
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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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".