Virtual reality and cardiac anatomy: Exploring immersive three‐dimensional cardiac imaging, a pilot study in undergraduate medical anatomy education
Bibliographic record
Abstract
Cardiac anatomy can be challenging to grasp because of its complex three-dimensional nature and remains one of the most challenging topics to teach. In light of some exciting technological advances in the field of virtual reality (VR), we sought to test the viability and the assess efficacy of this computer-generated model for the purposes of teaching cardiac anatomy. Before learning cardiac anatomy, first-year undergraduate medical students participated in an anatomically correct VR simulation of the heart. Students were randomly distributed into control and variable groups. Each student completed a pre-intervention quiz, consisting of 10 multiple choice questions with 5 conventional cardiac anatomy questions and 5 visual-spatial (VS) questions. The control group continued to independent study, whereas the variable group subjects were exposed to a 30-min immersive cardiac VR experience. At the end of the intervention, both the groups underwent a separate post-intervention 10-question quiz. Forty-two students participated in the cardiac VR experiment, separated into 14 control and 28 variable subjects. They scored 50.9% on average on the pre-intervention quiz (SD = 16.5) and 70.2% on the post-intervention quiz (SD = 18.7). Compared to the control group, the students exposed to VR scored 21.4% higher in conventional content (P = 0.004), 26.4% higher in VS content (P < 0.001), and 23.9% higher overall (P < 0.001). VR offers an anatomically correct and immersive VS environment that permits learner to interact three-dimensionally with the heart's anatomy. This study demonstrates the viability and the effectiveness of VR in teaching cardiac anatomy. Clin. Anat. 32:238-243, 2019. © 2018 Wiley Periodicals, Inc.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".