Accuracy of image-guided surgical navigation using near infrared (NIR) optical tracking
Bibliographic record
Abstract
Spinal surgery is particularly challenging for surgeons, requiring a high level of expertise and precision without being able to see beyond the surface of the bone. Accurate insertion of pedicle screws is critical considering perforation of the pedicle can result in profound clinical consequences including spinal cord, nerve root, arterial injury, neurological deficits, chronic pain, and/or failed back syndrome. Various navigation systems have been designed to guide pedicle screw fixation. Computed tomography (CT)-based image guided navigation systems increase the accuracy of screw placement allowing for 3- dimensional visualization of the spinal anatomy. Current localization techniques require extensive preparation and introduce spatial deviations. Use of near infrared (NIR) optical tracking allows for realtime navigation of the surgery by utilizing spectral domain multiplexing of light, greatly enhancing the surgeon’s situation awareness in the operating room. While the incidence of pedicle screw perforation and complications have been significantly reduced with the introduction of modern navigational technologies, some error exists. Several parameters have been suggested including fiducial localization and registration error, target registration error, and angular deviation. However, many of these techniques quantify error using the pre-operative CT and an intra-operative screenshot without assessing the true screw trajectory. In this study we quantified in-vivo error by comparing the true screw trajectory to the intra-operative trajectory. Pre- and post- operative CT as well as intra-operative screenshots were obtained for a cohort of patients undergoing spinal surgery. We quantified entry point error and angular deviation in the axial and sagittal planes.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.000 |
| 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".