Graphic and haptic modelling of the oesophagus for VR‐based medical simulation
Bibliographic record
Abstract
BACKGROUND: Medical simulators with vision and haptic feedback have been applied to many medical procedures in recent years, due to their safe and repetitive nature for training. Among the many technical components of the simulators, realistic and interactive organ modelling stands out as a key issue for judging the fidelity of the simulation. This paper describes the modelling of an oesophagus for a real-time laparoscopic surgical simulator. METHODS: For realistic simulation, organ deformation and tissue cutting in the oesophagus are implemented with geometric organ models segmented from the Visible Human Dataset. The tissue mechanical parameters were obtained from in vivo animal experiments and integrated with graphic and haptic devices into the laparoscopic surgical simulation system inside an abdominal mannequin. RESULTS: This platform can be used to demonstrate deformation and incision of the oesophagus by surgical instruments, where the user can haptically interact with the virtual soft tissues and simultaneously see the corresponding organ deformation on the visual display. CONCLUSIONS: Current laparoscopic surgical training has been transformed from the traditional apprenticeship model to simulation-based methods. The outcome of the model could replace conventional training systems and could be useful in effectively transferring surgical skills to novice surgeons.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| 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".