The Scanning Fiber Endoscope: A Novel Surgical and High-Resolution Imaging Device for Intracranial Neurosurgery
Bibliographic record
Abstract
BACKGROUND: The scanning fiber endoscope (SFE) is a novel medical imaging device that has been used in various vascular beds as a form of angioscopy, as well as in tracts and duct systems for endoluminal imaging. Owing to its miniaturized form, high resolution, and flexibility, it has demonstrated success in imaging across a wide range of diagnostic applications. OBJECTIVE: To demonstrate, by performing a third ventriculostomy and visualizing the cranial nerves and brainstem anatomy, that, without modification, the SFE can be used through a transcranial approach in a therapeutic intraventricular neurosurgical application. METHODS: A 3.7 French SFE system was used without modification on a live porcine model to perform a third ventriculostomy and acquire high-resolution images of the animal's ventricular system, cranial nerves, and brainstem. A side-by-side comparison was made with one of the current standard-of-care rigid endoscopes as a context for size and image quality. RESULTS: High-resolution video-rate imaging was used to assist the successful, uncomplicated performance of a third ventriculostomy. High-resolution endoscopic images of the brainstem and cranial nerves were acquired. CONCLUSION: Although the SFE has been shown to be a superior device for imaging, here we demonstrate its first use as a potential therapeutic device in intracranial neurosurgery.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 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".