Exploring the impact of virtual reality anatomy training on preparing biomedical illustrators for drawing anatomical structures
Bibliographic record
Abstract
Three-dimensional visualization technologies (3DVTs) in anatomy education are popular as they offer a cost-effective and accessible alternative to cadaveric specimens. However, the literature presents conflicting results regarding the effectiveness of 3DVTs in facilitating learning compared with traditional models. This study explores whether displaying 3D models using a virtual reality (VR) headset induces a stereoscopic experience comparable to that of physical models, by examining the quality of learners' depth perception as they reference different modalities to complete a series of illustrations. Using a crossover design, biomedical illustration trainers were randomly assigned to two groups and completed three illustrations using different reference modalities (2D, prosection, VR model). Illustrations were scored by subject matter experts using a validated scoring rubric and the mean scores for each modality were compared. Following their VR experience, participants completed a cybersickness and user experience survey. Participants (n = 17) were confirmed to have stereovision and average visuospatial ability. A two-way repeated measure ANOVA revealed a significant main effect of modality, where illustrations produced while referencing the 2D cadaveric image and prosection scored higher than those created using the VR model. Notably, participants demonstrated reduced ability in depicting depth of anatomical layers when referencing the VR model. Contrary to our hypothesis, the VR models did not provide similar quality of depth perception as prosection. Qualitative data suggest this may be a result of methodological challenges that increase cognitive processing demands on learners, potentially hindering learners' ability to interpret visuospatial cues.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".