Determination of tissue properties using microfabricated piezoelectric tactile sensor during minimally invasive surgery
Bibliographic record
Abstract
Purpose The purpose of this paper is to present the design, analysis, fabrication, and assembly of four tooth annular microfabricated tactile sensors integrated with the upper and lower jaws of an endoscopic surgical grasper tool, in order to determine the properties and particularly the compliance of the biological tissues during minimally invasive surgery. Design/methodology/approach A viscoelastic Kelvin model is employed for tissue characterization. A comprehensive closed form and finite element analysis has been carried out to express the relationship between the force ratio, compliance, and the equivalent viscous damping of the tissue. The designed sensor uses a polyvinyledene fluoride film as its sensing element. The sensor consists of arrays of rigid and compliant elements which are mounted on the tip of an endoscopic surgical grasper tool. Relative force between adjacent parts of the contact object is used to measure the viscoelastic properties. Findings The tactile sensor is able to characterize different viscoelastic properties of tissues. The experiments validate analytical and finite elements results. Practical implications The sensor is designed to integrate with the actual endoscopic tools to measure the softness of tissues. Originality/value A novel sensor‐tissue model is presented to characterize the variety of biological tissues.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".