High-Resolution Angioscopic Imaging During Endovascular Neurosurgery
Bibliographic record
Abstract
BACKGROUND: Endoluminal optical imaging, or angioscopy, has not seen widespread application during neurointerventional procedures, largely as a result of the poor imaging resolution of existing angioscopes. Scanning fiber endoscopes (SFEs) are a novel endoscopic platform that allows high-resolution video imaging in an ultraminiature form factor that is compatible with currently used distal access endoluminal catheters. OBJECTIVE: To test the feasibility and potential utility of high-resolution angioscopy with an SFE during common endovascular neurosurgical procedures. METHODS: A 3.7-French SFE was used in a porcine model system to image endothelial disruption, ischemic stroke and mechanical thrombectomy, aneurysm coiling, and flow-diverting stent placement. RESULTS: High-resolution, video-rate imaging was shown to be possible during all of the common procedures tested and provided information that was complementary to standard fluoroscopic imaging. SFE angioscopy was able to assess novel factors such as aneurysm base coverage fraction and side branch patency, which have previously not been possible to determine with conventional angiography. CONCLUSION: Endovascular imaging with an SFE provides important information on factors that cannot be assessed fluoroscopically and is a novel platform on which future neurointerventional techniques may be based because it allows for periprocedural inspection of the integrity of the vascular system and the deployed devices. In addition, it may be of diagnostic use for inspecting the vascular wall and postprocedure device evaluation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.001 | 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".