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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".