Hyperelastic modelling and parametric study of soft tissue embedded lump for MIS applications
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 it