Bringing Radiology Education to a New Reality: A Pilot Study of Using Virtual Reality as a Remote Educational Tool
Bibliographic record
Abstract
Purpose: We investigated virtual reality (VR) during a 2-week, undergraduate, radiology elective to determine if it improved learning outcomes and user satisfaction. Methods: Eighteen students enrolled between August 2021 and February 2022. Each student had a collaborative Zoom teaching session with a preceptor using a Picture Archive and Communications System (PACS)-like viewing system Online DICOM Image Navigator (ODIN), followed by a teaching session using a VR, Digital Imaging and Communications in Medicine (DICOM) viewer (SieVRt). After each teaching session, the students independently reviewed 8 imaging cases and completed case related questions. The students completed a survey, rating their subjective experiences using ODIN and SieVRt. Results: There was no difference in total test scores between the two learning strategies. However, students did perform statistically better on two of five questions designed to test the detection/measurement capabilities of SieVRt vs ODIN. Students stated that they preferred using SieVRt over ODIN and agreed that they were able to view subtle imaging findings and abnormalities better using SieVRt. However, students found that some of the functions of SieVRt (measuring angles/lengths, and multitasking) were difficult. There were technical challenges with VR and minor undesirable physical effects (dizziness, nausea, etc.). Conclusions: Virtual reality has the potential to enhance radiology education by providing an immersive and engaging experience. Objectively, students were able to perform two tasks better with SieVRt. Subjectively, the VR platform received favourable reviews from students for a variety of features. There were reported technical and physical challenges related to using VR. Future developments in VR systems should focus on improving the user experience.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.003 | 0.001 |
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".