Augmented real‐time navigation with critical structure proximity alerts for endoscopic skull base surgery
Bibliographic record
Abstract
OBJECTIVES/HYPOTHESIS: Image-guided surgery (IGS) systems are frequently utilized during cranial base surgery to aid in orientation and facilitate targeted surgery. We wished to assess the performance of our recently developed localized intraoperative virtual endoscopy (LIVE)-IGS prototype in a preclinical setting prior to deployment in the operating room. This system combines real-time ablative instrument tracking, critical structure proximity alerts, three-dimensional virtual endoscopic views, and intraoperative cone-beam computed tomographic image updates. STUDY DESIGN: Randomized-controlled trial plus qualitative analysis. METHODS: Skull base procedures were performed on 14 cadaver specimens by seven fellowship-trained skull base surgeons. Each subject performed two endoscopic transclival approaches; one with LIVE-IGS and one using a conventional IGS system in random order. National Aeronautics and Space Administration Task Load Index (NASA-TLX) scores were documented for each dissection, and a semistructured interview was recorded for qualitative assessment. RESULTS: The NASA-TLX scores for mental demand, effort, and frustration were significantly reduced with the LIVE-IGS system in comparison to conventional navigation (P < .05). The system interface was judged to be intuitive and most useful when there was a combination of high spatial demand, reduced or absent surface landmarks, and proximity to critical structures. The development of auditory icons for proximity alerts during the trial better informed the surgeon while limiting distraction. CONCLUSIONS: The LIVE-IGS system provided accurate, intuitive, and dynamic feedback to the operating surgeon. Further refinements to proximity alerts and visualization settings will enhance orientation while limiting distraction. The system is currently being deployed in a prospective clinical trial in skull base surgery.
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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 | 0.002 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".