Spinal Needle Navigation by Tracked Ultrasound Snapshots
Bibliographic record
Abstract
PURPOSE: Ultrasound (US) guidance in facet joint injections has been reported previously as an alternative to imaging modalities with ionizing radiation. However, this technique has not been adopted in the clinical routine, due to difficulties in the visualization of the target joint in US and simultaneous manipulation of the needle. METHODS: We propose a technique to increase targeting accuracy and efficiency in facet joint injections. This is achieved by electromagnetically tracking the positions of the US transducer and the needle, and recording tracked US snapshots (TUSS). The needle is navigated using the acquired US snapshots. RESULTS: In cadaveric lamb model, the success rate of facet joint injections by five orthopedic surgery residents significantly increased from 44.4% with freehand US guidance to 93.3% with TUSS guidance. Needle insertion time significantly decreased from 47.9 ± 34.2 s to 36.1 ± 28.7 s (mean ± SD). In a synthetic human spine model, a success rate of 96.7% was achieved with TUSS. The targeting accuracy of the presented system in a gel phantom was 1.03 ± 0.48 mm (mean ± SD). CONCLUSION: Needle guidance with TUSS improves the success rate and time efficiency in spinal facet joint injections. This technique readily translates also to other spinal needle placement applications.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".