Computer‐assisted pedicle screw trajectory planning using CT‐inferred bone density: A demonstration against surgical outcomes
Bibliographic record
Abstract
PURPOSE: Image-guided spine surgery and preoperative computer-assisted planning provide spine surgeons with tools to improve the safety, accuracy, and reliability of pedicle screw placement. The purpose of this study is to demonstrate a computer-assisted pedicle screw placement planning tool in comparison to screws as delivered by a spine surgeon. METHODS: We describe a novel computer-assisted tool for preoperative pedicle screw placement planning in computed tomography (CT) images, designed with respect to the vertebral shape and structure, and augmented with respect to the considerations of surgical practice. The approach is based on three-dimensional (3D) modeling of the vertebral body and pedicles, and planning of the pedicle screw size and insertion trajectory by maximizing the screw fastening strength, evaluated through CT-inferred bone density maps. The approach is augmented by yielding screw plans consistent with the straight-forward surgical technique of aligning screws parallel to vertebral endplates, and the screw entry points following the spinal curvature to facilitate rod attachment. For a cohort of 25 patients, placement plans were retrospectively obtained for 204 pedicle screws with the computer-assisted tool from preoperative CT images, while reference trajectories of inserted pedicle screws were reconstructed in 3D from postoperative biplanar radiographs. RESULTS: for the screw sagittal inclination (mean absolute difference ± standard deviation). CONCLUSION: Quantitative comparison revealed that the preoperative placement plans are consistent with the postoperative results, and that the computer-assisted tool integrating bone density and surgical constraints can successfully incorporate important aspects of pedicle screw placement. The results therefore confirm the accuracy of the tool prior to being integrated in an image-guidance system.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".