Virtual Fluoroscopy: Computer-Assisted Fluoroscopic Navigation
Bibliographic record
Abstract
STUDY DESIGN: In vitro accuracy assessment of a novel virtual fluoroscopy system. OBJECTIVES: To investigate a new technology combining image-guided surgery with C-arm fluoroscopy. SUMMARY OF BACKGROUND DATA: Fluoroscopy is a useful and familiar technology to all musculoskeletal surgeons. Its limitations include radiation exposure to the patient and operating team and the need to reposition the fluoroscope repeatedly to obtain surgical guidance in multiple planes. METHODS: Fluoroscopic images of the lumbar spine of an intact, unembalmed cadaver were obtained, calibrated, and saved to an ). A was used for the sequential insertion of a light-emitting diode-fitted probe into the pedicles of L1-S1 bilaterally. The trajectory of a "virtual tool" corresponding to the tracked tool was overlaid onto the saved fluoroscopic views in real time. Live fluoroscopic images of the inserted pedicle probe were then obtained. Distances between the tips of the virtual and fluoroscopically displayed probes were quantified using the image-guided computer's measurement tool. Trajectory angle differences were measured using a standard goniometer and printed copies of the workstation computer display. The surgeon's radiation exposure was measured using thermolucent dosimeter rings. RESULTS: Excellent correlation between the virtual fluoroscopic images and live fluoroscopy was observed. Mean probe tip error was 0.97 +/- 0.40 mm. Mean trajectory angle difference between the virtual and fluoroscopically displayed probes was 2.7 degrees +/- 0.6 degrees. The thermolucent dosimeter rings measured no detectable radiation exposure for the surgeon. CONCLUSIONS: Virtual fluoroscopy offers several advantages over conventional fluoroscopy while providing acceptable targeting accuracy. It enables a single C-arm to provide real-time, multiplanar procedural guidance. It also dramatically reduces radiation exposure to the patient and surgical team by eliminating the need for repetitive fluoroscopic imaging for tool placement.
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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 | 0.003 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".