Virtual reality myringotomy simulation with real‐time deformation: Development and validity testing
Bibliographic record
Abstract
OBJECTIVES/HYPOTHESIS: Surgical simulation is becoming an increasingly common training tool in residency programs. The first objective was to implement real-time soft-tissue deformation and cutting into a virtual reality myringotomy simulator. The second objective was to test the various implemented incision algorithms to determine which most accurately represents the tympanic membrane during myringotomy. STUDY DESIGN: Descriptive and face-validity testing. METHODS: A deformable tympanic membrane was developed, and three soft-tissue cutting algorithms were successfully implemented into the virtual reality myringotomy simulator. The algorithms included element removal, direction prediction, and Delaunay cutting. The simulator was stable and capable of running in real time on inexpensive hardware. A face-validity study was then carried out using a validated questionnaire given to eight otolaryngologists and four senior otolaryngology residents. Each participant was given an adaptation period on the simulator, was blinded to the algorithm being used, and was presented the three algorithms in a randomized order. RESULTS: A virtual reality myringotomy simulator with real-time soft-tissue deformation and cutting was successfully developed. The simulator was stable, ran in real time on inexpensive hardware, and incorporated haptic feedback and stereoscopic vision. The Delaunay cutting algorithm was found to be the most realistic algorithm representing the incision during myringotomy (P < .05). The Likert and visual analog scales had strong correlations, suggesting good internal reliability. CONCLUSIONS: The first virtual reality myringotomy simulator is being developed and now integrates a real-time deformable tympanic membrane that appears to have face validity. Further development and validation studies are necessary before the simulator can be studied with respect to training efficacy and clinical impact.
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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 | 0.000 |
| 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".