A novel tactile probe with medical and surgical applications
Bibliographic record
Abstract
Purpose The paper aims to discuss design, fabrication, testing and simulation of a novel tactile probe used for measuring the stiffness of biological soft tissues/materials with a view to medical and surgical applications. Design/methodology/approach Both finite element modeling and experimental approach were used in this research. The novel tactile probe capable of recording force-deformation feedback is accompanied with the tactile-status-display which is a custom-designed user-friendly interface. This system can evaluate the stiffness in each part of force-deformation status. Findings The new system named novel tactile probe was fabricated, and the results on artificial materials (with different stiffnesses) and the sheep kidney (containing a hard object) were reported. Recording different stiffnesses, detecting hard object embedded in soft tissue and predicting the exact location of it are the main results that have been extracted through the diagrams obtained by the novel tactile probe system. Research limitations/implications The designed and fabricated system can be modified and miniaturized to be used during different minimally invasive surgeries in the future. Practical implications The most distinguishing feature of this novel tactile probe is its applicability during different laparoscopic surgeries, so the in vivo data can be obtained. Originality/value For the first time, a tactile probe has been designed and tested in the form of laparoscopic instrument which upgrades the efficiency of available laparoscopic instruments. Also, the novel tactile probe can be used in both in vivo and in vitro experimental setups for measuring the stiffness of sensed objects.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".