Accuracy of pedicle localization using a 3D ultrasound navigator on vertebral phantoms for posterior spinal surgery
Bibliographic record
Abstract
Severe adolescent idiopathic scoliosis (AIS) requires surgery to halt curve progression. Accurate insertion of pedicle screws is important. This study reports a newly developed 3D ultrasound (3DUS) to localize pedicles intraoperatively and register a pre-op 3D vertebral model to the surface to be displayed for navigation. The objective was to determine speed of the custom 3DUS navigator and accuracy of pedicle probe placement. The developed 3DUS navigator integrated an ultrasound scanner with motion capture cameras. Two adolescent 3D printed spine models T2-T8 and T7-T11 were modified to include pedicle holes with known trajectory and be mounted on a high precision LEGO pegboard in a water bath for imaging. Calibration of the motion cameras and the 3DUS were conducted prior to the study. A total of 27 scans from T3 to T11 vertebrae with 3 individual scans were performed to validate the repeatability. Three accuracy tests that varied vertebral a) orientation, b) position and c) a combination of location and orientation were completed. Based on all experiments, the acquisition-to-display time was 18.9±3.1s. The repeatability of the trajectory error and positional error were 0.5±0.2° and 0.3±0.1mm, respectively. The a) center orientation, b) position and c) orientation/position on trajectory and positional error were for a) 1.4±0.9° and 0.5±0.4mm, b) 1.4±0.8° and 0.3±0.3mm and c) 2.0±0.8° and 0.5±0.5mm, respectively. These results demonstrated that a high precision real-time 3DUS navigator for screw placement in scoliosis surgery is feasible. The next step will study the effect of surrounding soft tissues on navigation accuracy.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".