Evaluating the integration of body donor imaging into anatomical dissection using augmented reality
Bibliographic record
Abstract
Augmented reality (AR) has recently been utilized as an integrative teaching tool in medical curricula given its ability to view virtual objects while interacting with the physical environment. The evidence for AR in medical training, however, is limited. For this reason, the purpose of this mixed method study was to evaluate the implementation of overlaying donor-specific diagnostic imaging (DSDI) onto corresponding body donors in a fourth-year, dissection-based, medical elective course entitled anatomy for surgeons (AFS). Students registered in AFS course were separated into groups, receiving either DSDI displayed on Microsoft HoloLens AR head-mounted display (n = 12) or DSDI displayed on iPad (n = 15). To test for the change in spatial ability, students completed an anatomical mental rotation test (AMRT) prior to and following the AFS course. Students also participated in a focus group discussion and completed a survey at the end of AFS, analyzed through thematic triangulation and an unpaired, Mann Whitney U test respectively, both addressing dissection experience, DSDI relevancy to dissection, and use of AR in anatomical education. Although statistically significant differences were not found when comparing student group AMRT scores, survey and discussion data suggest that the HoloLens had improved the students' understanding of, and their spatial orientation of, anatomical relationships. Trunk dissection quality grades were significantly higher with students using the HoloLens. Although students mentioned difficulties with HoloLens software, with faculty assistance, training, and enhanced software development, there is potential for this AR tool to contribute to improved dissection quality and an immersive learning experience.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".