Towards the development of a mixed reality haptic temporal bone surgical simulation
Bibliographic record
Abstract
The temporal bone is an anatomically complex region within the skull. Current training for temporal bone surgery includes cadaveric, physical and virtual haptic simulations and apprenticeships. Cadavers are limited by low supply. Haptic devices are limited by their force and stiffness ratings and thus cannot adequately simulate rigid materials. Physical simulations excel at simulating stiff materials but do a poor job of soft tissue. The research objective was to develop a mixed reality (MR) temporal bone surgical haptic simulation. This novel concept would utilize physical models to simulate bone and haptic forces to simulate soft tissue. A surgical drill was attached to a Quanser® High Definition Haptic DeviceTM (HD2) via a clamp. An algorithm was implemented to simulate a force at the drill tip and to negate the weight of the clamp. This modified haptic system was interfaced to a temporal bone haptic simulation. Haptic chatter unique to the modified haptic system was observed and low-pass filters were used to mitigate this issue. Due to the poor positional accuracy of the HD2, MR simulation was not achieved. However, VR haptic simulation was achieved. Six expert surgeons were recruited to investigate the following questions: "What is the impact of different haptic hardware on surgical realism?" and "Would end users prefer a surgical drill over a standard haptic manipulandum?" Three cases were compared: a Phantom Omni®, a standard HD2 and a modified HD2 with attached drill. Expert surgeons rated the standard HD2 and Phantom Omni equivalently whilst preferring the modified HD2 with attached drill. Though the modified HD2 scored higher in all categories only “Acoustics” and “Overall Appreciation” displayed statistical significance. This implies that drill acoustics is critical for realism.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".