Graphical representation of tactile sensing data in minimally invasive surgery
Bibliographic record
Abstract
Nowadays, Minimally Invasive Surgery (MIS) has gained enormous popularity among the surgeons and in teleoperation procedures such as Robotic Assisted Minimally Invasive Surgery and Tele-Robotic Minimally Invasive Surgery. Despite its many advantages, MIS decreases the tactile sensory perception of the surgeon during grasping or manipulation of biological tissues. The loss of tactile perception has attracted a lot of attention. In this paper, we describe the detailed design of the hardware and software system used for graphical representation of the tactile sensing data. The proposed hardware and data acquisition system receives signals from the sensors incorporated on the MIS grasper and then transmits them to a personal computer. The developed software is also presented and its capabilities are discussed accordingly. Using this designed system, it is possible to determine the softness of the grasped objects. The degree of the softness is also displayed visually on the computer. The tactile sensor consists of four sensing elements which are integrated in each jaw of a modified commercial endoscopic grasper. Each sensing element consists of two piezoelectric polymer Plyvinylidene Fluoride (PVDF) films. The combination of the output voltages from each sensing element is used to determine the softness of the grasped object.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.050 | 0.008 |
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".