Design and Development of a Tele-operated Surgical Simulation Environment
Bibliographic record
Abstract
With the introduction of robots into laparoscopic surgery, surgeons have difficulties in selecting the placement of the incisions required to insert the robots instruments into the body and also determine which patients are suitable for robotically assisted surgery. Poor selection of these two items mentioned above can result in a conversion to a more invasive form of surgery during the procedure. This work introduces the design and development of a surgical simulation environment to assist in the research for optimal incision placement and patient selection.\nThe simulator allows importing any serial link robot that was designed in a computer aided modelling package. With minimal added information, the imported robot can be controlled using a multi-degree of freedom user input device. The simulator allows for importing patient geometries along with the robot to allow for the simulation of surgical procedures. A Jacobian transpose algorithm was added onto the simulator in a modular format to control the simulated robots, as well as to allow for other control systems to be created and implemented.\nExperiments were performed to determine the effects of patient geometry models on rendering speeds. The control system could control the tested robots with a maximum lag time of 15 ms between moving the input device and the simulated robot moving to the correct desired position.\nThe simulator makes importing and controlling robots a simple and intuitive matter, without putting a large restriction on the type of robots to be simulated. The simulator also allows for importing models of a patient, to make real world analysis of a patient possible. Further improvements on the presented simulator include the addition of collision detection and more testing on the control system for stability and response over a larger range of robots.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".