A Virtual-Reality Training Simulator for Cochlear Implant Surgery
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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 it