Hyperelastic modelling and parametric study of soft tissue embedded lump for MIS applications
Bibliographic record
Abstract
BACKGROUND: The existing MIS (minimally invasive surgery) instruments have caused severe restrictions to surgeons' tactile perception. In particular, palpation, which is an important technique in open surgery to assess the softness of the tissue and to detect any hidden lumps, is entirely absent in MIS procedures. Many researchers have developed smart endoscopic graspers to rectify different aspects of this problem. However, the effect of an anatomical feature in general and a lump in particular on the stress distribution on the sensitive surfaces of the smart MIS graspers still needs a lot of attention. METHODS: This paper investigates the effect of the important parameters of a lump on the stress distribution at the contact surface and subsequently the output of smart endoscopic graspers. Using experimental stress-strain compression test data, the material parameters required for the Mooney-Rivlin model were obtained and used in hyperelastic finite element analysis. RESULTS: The influence of size, depth and stiffness of the lump on the stress distribution at the contact surface are shown and discussed. The results of the non-linear finite element analysis were validated against experiments conducted on elastomeric material replicating soft tissue. CONCLUSIONS: The consistency between finite element analysis results and experimental work validates the developed model, which is based on the hyperelastic formulation. The finite element analysis results obtained in this study are particularly useful for the development of an inverse model. The inverse model would extract fundamental information, such as size, depth and stiffness, of any hidden lump, using the outputs of the sensors.
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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 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".