Design of Medical Simulators in Obstetrics
Bibliographic record
Abstract
Minimally Invasive Surgery represents the future of many types of medical inter- ventions such as keyhole neurosurgey or transluminal endoscopic surgery. These procedures involve insertion of surgical instruments such as needles and endoscopes into human body through small incision/ body cavity for biopsy and drug delivery. However, nearly all surgical instruments for these procedures are inserted manu- ally and there is a long learning curve for surgeons to use them properly. Many research efforts have been made to design active instruments (endoscope, needles) to improve this procedure during last decades. New robot mechanisms have been used to improve the dexterity of current endoscope. In recent years, several con- tinuum robots have been investigated as mechanisms for medical surgery. Those are characterized by the fact that their mechanical components do not have rigid links and discrete joints in contrast with traditional robot manipulators. The de- sign of these robots are inspired by movements of natural animals such as tongues, elephant trunks and tentacles. The unusual compliance and redundant degrees of freedom of these robots provide strong potential to achieve delicate tasks success- fully even in cluttered and unstructured environments. This chapter will present a complete application of continuum robot, called Colobot, for Minimally Invasive Surgery of colonoscopy. This system is composed of a micro-robotic tip, a set of position sensors and a real-time control system for guiding the exploration of colon. Details will be described on the modeling of the pneumatic actuators used, the design of the mechanical component, the kinematic model analysis and the control strategy for automatically guiding the progression of the device inside the human colon. Experimental results will be presented to check the performances of the whole system within a transparent tube.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".