Virtual and Augmented Reality in Ventriculostomy: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Ventriculostomy, one of the most common neurosurgical procedures, involves inserting a draining catheter into the brain's ventricular system to alleviate excessive cerebrospinal fluid accumulation. Traditionally, this procedure has relied on freehand techniques guided by anatomical landmarks, which have shown a high rate of misplacement. Recent advancements in virtual reality (VR) and augmented reality (AR) technologies have opened up new possibilities in the field. This comprehensive review aims to analyze the existing literature, examine the diverse applications of VR and AR in ventriculostomy procedures, address their limitations, and propose potential future directions. METHODS: A systematic search was conducted in Web of Science and PubMed databases to identify studies employing VR and AR technologies in ventriculostomy procedures. Review papers, non-English records, studies unrelated to VR/AR technologies in ventriculostomy, and supplementary documents were excluded. In total 29 papers were included in the review. RESULTS: The development of various VR and AR systems aimed at enhancing the ventriculostomy procedure are categorized according to the Data, Visualization and View taxonomy. The study investigates the data utilized by these systems, the visualizations employed, and the virtual or augmented environments created. Furthermore, the surgical scenarios and applications of each method, as well as the validation and evaluation metrics used, are discussed. DISCUSSION: The review delves into the fundamental challenges encountered in the implementation of VR and AR systems in ventriculostomy. Additionally, potential future directions and areas for improvement are proposed, addressing the identified limitations and paving the way for further advancements in the field.
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 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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".