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 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 | 0.009 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".