Evaluating the integration of body donor imaging into anatomical dissection using augmented reality
Bibliographic record
Abstract
Recent advancements in anatomy education have incorporated the use of augmented reality (AR) into medical curricula. AR has begun to emerge as a particularly useful tool since students can overlay diagnostic imaging (ex: MRI, CT scans) directly onto an anatomical specimen or model. Studies evaluating the use of the Microsoft HoloLens, a brand of AR smart glasses, in anatomical education have suggested the benefits of this tool mainly for self‐study while also describing its overall use as difficult and pointing out the necessity for technical support. Many of these studies, however, did not use objective measures to assess the modality’s implementation and/or use. The purpose of this investigation to analyze the effects of the AR modality on student learning and cadaveric dissection experience into a fourth‐year dissection‐based medical course offered at McGill University. A convergent parallel mixed methods approach was used comprising of both quantitative and qualitative data collection phases. Students registered in the course were separated into two groups, one group receiving diagnostic imaging to view on a HoloLens device and the other group on an iPad. Student responses to a study participant questionnaire and anatomical mental rotation test (AMRT), assessing spatial ability, were evaluated quantitatively. Qualitative data included written transcripts from focus group interviews conducted with both study groups following the course. Survey results were analyzed and compared across study groups using non‐parametric statistics; an unpaired, Mann Whitney U test. AMRT data was evaluated using parametric statistical analyses; one‐way ANOVA with Sidak’s post‐hoc test. Qualitative data was analyzed using inductive and deductive coding, followed by thematically organizing student responses from focus group interviews into relevant themes. IRB# A12‐E82‐17B. Overall, students in the HoloLens group expressed difficulty using the HoloLens to project body donor imaging and understanding the interaction between the projected imaging with their dissection, in comparison to their iPad group counterparts:. These findings were all statistically significant. Additionally, AMRT data showed no statistically significant differences between groups, both pre‐ and post‐AFS. HoloLens students were also more inclined to agree their imaging modality motivated their learning. In the focus group interviews, students also shared that the incorporation of radiology in AFS and the HoloLens device had a positive impact on their anatomy education. The HoloLens AR device in this investigation was able to increase student motivation, promote appreciation of the imaging overlay and provide an enhanced dissection experience. For these reasons, this investigation shows promise that AR can successfully be used in anatomical medical curricula. Support or Funding Information The authors would like to thank the support provided by the Dr. Clarke K. McLeod Memorial Scholarship (to KM) and Class of Medicine 1974 Faculty Scholar for Teaching Excellence & Innovation in Medical Education as well as The Centre for Medical Education Innovation and Research Seed Fund (to GPJCN).
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".