Virtual Reality, Autostereoscopy, and Physical Models for Learning Anatomy: Performance Comparisons
Bibliographic record
Abstract
Introduction Traditional anatomical education relies on textbooks, physical models, and cadaveric specimens. Advances in technology have provided other display modalities including virtual reality (VR), and autostereoscopic displays. VR uses head‐mounted displays (HMDs) for an interactive experience and autostereoscopic screens, like Alioscopy TM , offer a stereoscopic but HMD‐free environment. Current research shows contradictory results on the efficacy of VR compared to physical models, and there appears to be no data on the efficacy of autostereoscopic displays in learning anatomy. The purpose of this study is to determine whether VR, Alioscopy TM , or physical models yield the best performance for anatomical education. Methods Students at McMaster University without prior anatomy training will learn nominal skeletal anatomy in three different modalities: VR (Oculus Quest 2 TM ), Alioscopy TM , or a physical 3D‐printed bone model. Each of the environments will be as identical as possible (i.e, the VR environment is a rendering of the exact room and set‐up used for testing the physical bone models). Participants will be randomized to one of three interventions where they will study ten bony landmarks on either the human hemipelvis, zygomatic bone, or calcaneus in a distinct modality. Participants will be seated and use an Xbox TM controller to rotate the bone along the vertical axis of rotation. After four minutes, an untimed, recognition‐based test will be administered, where participants will be given a 3D‐printed bone identical to the one used in the learning phase, with randomized landmarks and word bank of landmarks learnt. Performance will be evaluated based on landmarks correctly identified on the recognition‐based test. Results Based on the current literature, we hypothesize that the physical models will be a superior learning environment compared to the VR environment. However, significant improvements in the HMD of the Oculus Quest 2 may have ameliorated previous issues with the VR environment. There is no data available on the efficacy of Alioscopy TM and while the absence of an HMD is appealing, its narrow viewing angle compared to the immersive VR environment may be problematic. Conclusion With the push to increase accessibility and decrease costs associated with anatomical education with the help of digital modalities, the findings from this study are critical to informing teaching practices, and technology use in anatomy education.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 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".