Empowering Children Through Virtual Reality: A New Alternative to General Anesthesia for MRI
Bibliographic record
Abstract
BACKGROUND: MRI often requires general anesthesia in children, which carries risks, increases costs, and prolongs scan wait times. PURPOSE: Our study aimed to evaluate whether virtual reality (VR) simulations could familiarize children with the MRI experience to enable awake scans without anesthesia. Secondary objectives included assessing child anxiety and determining whether movement during the simulation correlated with scan quality. MATERIALS AND METHODS: In this prospective study, 18 participants underwent a 10-minute VR simulation of an MRI procedure presented as an avatar-led game before their head MRI scan. Child and caregiver anxiety surveys were completed before the simulation and after the MRI. The VR software recorded head motion during the simulation, which was correlated with MRI scan quality. RESULTS: All participants (n = 18) successfully completed an awake MRI after the simulation session, aiding clinical diagnoses. The average participant age was 5.0 years (±1.3 years). MRI quality assessments indicated 44.4% excellent, 27.8% high-acceptable, 22.2% acceptable, and 5.6% low-acceptable scan quality. No statistically significant changes in anxiety levels were observed. 94.1% of legal guardians reported the VR simulation was effective at preparing their child for the MRI scan. CONCLUSION: VR sessions were associated with a significant improvement in caregiver perceptions and enabled successful completion of MRI scans without the need for sedation in all children initially considered to require anesthesia. While no statistically significant reduction in anxiety was observed, the intervention resulted in diagnostic-quality imaging with minimal motion artifacts, supporting its utility as a strategy to facilitate pediatric MRI without anesthesia.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".