Augmented image guidance improves skull base navigation and reduces task workload in trainees: A preclinical trial
Bibliographic record
Abstract
OBJECTIVES/HYPOTHESIS: Our group has developed an augmented image guidance system that incorporates intraoperative cone-beam computed tomography (CBCT), virtual or augmented displays, and image registration. We assessed the potential benefits of augmented endoscopy derived from this system for use during skull base navigation. Specifically, we wished to evaluate target localization accuracy and the effect on task workload and confidence. STUDY DESIGN: Prospective, sequential, paired preclinical trial. METHODS: A single cadaver head underwent computed tomography, and critical structures were contoured. The specimen was reimaged after endoscopic dissection and deformable registration allowed contours to be displayed on postablation CBCT imaging. A real-time virtual view including anatomical contours was provided parallel to the real endoscopic image. Twelve subjects were asked to endoscopically localize seven skull base landmarks in a conventional manner. The same exercise was then performed with augmented endoscopy. Precise three-dimensional (3D) localization was recorded with a tracked probe. The NASA task load index was completed after each exercise. A short questionnaire was also administered. RESULTS: The real-time augmented image guidance system aided localization in 85% of responses and increased confidence in 97%. There was a significant reduction in mental demand, effort, and frustration when the technology was employed, with an increase in perceived performance (P < .05). Three dimensional navigational precision was improved for all landmarks. CONCLUSIONS: Real-time augmented image-guided surgery increases accuracy and confidence in trainee surgeons and decreases task workload during skull base navigation. This technology shows great promise in assisting in skull base surgery even for experienced surgeons.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".