Applications of a Novel Microangioscope for Neuroendovascular Intervention
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Visualization in neuroendovascular intervention currently relies on biplanar fluoroscopy and contrast administration. With the advent of endoscopy, direct visualization of the intracranial intravascular space has become possible with microangioscopes. We analyzed the efficacy of our novel microangioscope to enable direct observation and inspection of the cerebrovasculature, complementary to a standard fluoroscopic technique. MATERIALS AND METHODS: Iterations of microangioscopes were systematically evaluated for use in neurodiagnostics and neurointerventions in both live animal and human cadaveric models. Imaging quality, trackability, and navigability were assessed. Diagnostic procedures assessed included clot identification and differentiation, plaque identification, inspection for vessel wall injury, and assessment of stent apposition. Interventions performed included angioscope-assisted stent-retriever thrombectomy, clot aspiration, and coil embolization. RESULTS: The microangioscope was found helpful in both diagnosis and interventions by independent evaluators. Mean ratings of the imaging quality on a 5-point scale ranged from 3.0 (clot identification) to 4.7 (Pipeline follow-up). Mean ratings for clinical utility ranged from 3.0 (aspiration thrombectomy) to 4.7 (aneurysm treatment by coil embolization and WEB device). CONCLUSIONS: This fiber optic microangioscope can safely navigate and visualize the intravascular space in human cadaveric and in vivo animal models with satisfactory resolution. It has potential value in diagnostic and neurointerventional applications.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".