Augmented Reality Visualization–guided Microscopic Spine Surgery: Transvertebral Anterior Cervical Foraminotomy and Posterior Foraminotomy
Bibliographic record
Abstract
OBJECTIVE: We describe intraoperative augmented reality (AR) imaging to obtain a microscopic view in spine keyhole surgery. BACKGROUND: Minimally invasive keyhole surgery has been developed even for spine surgery, including transvertebral anterior cervical foraminotomy and posterior cervical laminoforaminotomy. These methods are complex and require a skillful technique. Therefore, inexperienced surgeons hesitate to perform keyhole surgeries. The technology used in surgery is rapidly advancing, including intraoperative imaging devices that have enabled AR imaging and facilitated complicated surgeries in many fields. However, data are not currently available on the use of AR imaging in spine surgery. The purpose of this article was to introduce the utility of AR for spine surgery. METHODS: We performed O-arm intraoperative imaging to create an augmented imaging model in navigation systems. Navigation data were linked to a microscope to merge the live view and AR. Augmented reality imaging shows the model plan in the real-world surgical field. We used this novel method in patients who underwent both keyhole surgeries. RESULTS: We successfully performed both surgeries using the AR visualization guide. CONCLUSIONS: The AR navigation system facilitates complicated keyhole surgeries in patients who undergo spine surgery. STUDY DESIGN: Technical report.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".