Bibliographic record
Abstract
Virtual endoscopy is the computerized creation of images depicting the inside of patient anatomy reconstructed in a virtual reality environment. It permits interactive, noninvasive, 3-dimensional visual inspection of anatomical cavities or vessels. This can aid in diagnostics, potentially replacing an actual endoscopic procedure, and help in the preparation of a surgical intervention by bridging the gap between plain 2-dimensional radiologic images and the 3-dimensional depiction of anatomy during actual endoscopy. If not only the endoscopic vision but also endoscopic handling, including realistic haptic feedback, is simulated, virtual endoscopy can be an effective training tool for novice surgeons. In neurosurgery, the main fields of the application of virtual endoscopy are third ventriculostomy, endonasal surgery, and the evaluation of pathologies in cerebral blood vessels. Progress in this very active field of research is achieved through cooperation between the technical and the medical communities. While the technology advances and new methods for modeling, reconstruction, and simulation are being developed, clinicians evaluate existing simulators, steer the development of new ones, and explore new fields of application. This review introduces some of the most interesting virtual reality systems for endoscopic neurosurgery developed in recent years and presents clinical studies conducted either on areas of application or specific systems. In addition, benefits and limitations of single products and simulated neuroendoscopy in general are pointed out.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".