Three-dimensional rotational angiography guidance for aneurysm surgery
Bibliographic record
Abstract
OBJECT: The aim of this study was to investigate the feasibility of integrating three-dimensional rotational angiography (3D-RA) data into a surgical navigation system and to assess its accuracy and potential clinical benefit. METHODS: The study cohort consisted of 16 patients with 16 intracranial aneurysms who had been scheduled for routine or emergency surgery. Rotational angiography data were exported using a virtual reality modeling language file format and imported into the BrainLAB VectorVision2 image-guided surgery equipment. During 3D-RA the position of the head was measured using a special headframe. The authors also determined the accuracy of 3D-RA image guidance and the clinical benefit as judged by the surgeon, including, for example, early identification of branching vessels and the aneurysm. There was good correspondence between the 3D-RA-based navigation data and the intraoperative vascular anatomy in all cases, with a maximum error of 9 degrees of angulation and 9 degrees of rotation. In eight cases, the surgeon determined that the 3D-RA image guidance facilitated the surgical procedure by predicting the location of the aneurysm or the origin of a branching artery that had been covered by brain tissue and blood clots. CONCLUSIONS: The integration of 3D-RA into surgical navigation systems is feasible, but it currently requires a new perspective-registration technique. The intraoperative 3D view provides useful information about the vascular anatomy and may improve the quality of aneurysm surgery in selected cases.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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".