A Virtual-Reality Training Simulator for Cochlear Implant Surgery
Bibliographic record
Abstract
Background and Objectives. Hearing loss is one of the most prevalent chronic conditions and can significantly impact an individual’s quality of life. Cochlear implantation (CI) is a widely applicable treatment for severe to profound hearing loss, however CI surgery can be difficult for surgical trainees to master. Training environments that are safe, controlled, and affordable are needed. To this end, we present a virtual-reality (VR) cochlear implant surgical simulator developed with a popular, commercial game engine. Method. Unity3D was used to develop the simulator and model the delicate instruments involved. High-resolution models of human cochleae were created from images obtained from synchrotron-radiation phase-contrast imaging (SR-PCI). The physical-realism of the simulator was assessed via a comparison with fluoroscopic images of an actual cochlear implant insertion. Different resolutions of cochlear models were used to benchmark the real-time capabilities of the simulator with the number of frames per second (FPS) serving as the performance metric. Results. Quantitative analysis comparing the simulated procedure to fluoroscopic imaging revealed no significant differences. Qualitatively, the behaviour of the inserted and simulated implants were similar throughout the entirety of the procedure. The simulator was able to maintain 25 FPS even when experiencing an artificially high computational load. Conclusion. VR simulators provide a new and exciting avenue to enhance current medical education. Continued use of widely available and supported game engines in the development of medical simulators will hopefully result in lowered costs. Preliminary feedback from expert surgeons of the simulator presented here has been positive and future work will focus on evaluating face, content and construct validity.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".