Comparing the Efficacy of Radiation Free Machine-Vision Image-Guided Surgery With Traditional 2-Dimensional Fluoroscopy: A Randomized, Single-Center Study
Bibliographic record
Abstract
Background: Three-dimensional (3D) computer-assisted navigation (CAN) has emerged as a potential alternative to 2-dimensional (2D) fluoroscopy in the surgical placement of spinal instrumentation. Recently, 3D-CAN systems have improved significantly in their ability to provide real-time anatomical referencing while shortening the registration and set-up time. A novel system in navigation, Machine-Vision Image-Guided Surgery (MvIGS; 7D Surgical, Toronto, Canada) was cleared by the US Food and Drug Administration, but its potential benefits in reducing intra-operative radiation exposure to patients and enhancing surgical accuracy of pedicle screw placement are not fully known. Purpose: We sought to conduct a prospective, randomized, clinical study comparing the 3D-MvIGS spinal navigation system and 2D-fluoroscopy for pedicle screw insertion up to 3 levels (T10-S1) and for various measures of surgical efficacy. Methods: Sixty-two eligible patients were randomized to receive spine surgery using either the 3D-MvIGS group or the conventional 2D-fluoroscopy for pedicle screw fixation for the treatment of spinal stenosis and degenerative spondylolisthesis. Intra-operative parameters and procedure-related unintended protocol violations were recorded. Results: Operative time and estimated blood loss were not significantly different between groups. Radiation time and exposure to patients were significantly reduced in the 3D-MvIGS group. There was no difference between groups in pedicle screw placement accuracy (2D-fluoroscopy group, 96.6%; 3D-MvIGS group, 94.2%). There were no major complications or cases that required revision surgery. Conclusion: The 3D-MvIGS navigation system performed comparably with 2D-fluoroscopy in terms of pedicle screw placement accuracy and operative time. The 3D-MvIGS showed a significant reduction in radiation exposure to patients. In more complex cases or larger cohorts, the true value of greater anatomical visualization can be elucidated.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".