Mechanics of Tissue Cutting During Needle Insertion in Biological Tissue
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Bibliographic record
Abstract
In percutaneous needle insertions, cutting forces at the needle tip deflect the needle and increases targeting error. Thus, modeling needle-tissue interaction in biological tissue is essential for accurate robotics-assisted needle steering. In this letter, dynamics of needle tip interaction with inhomogeneous biological tissue is described and the effects of insertion velocity, tissue mechanical characteristics, and needle geometry on tissue cutting force are studied. Needle interaction with biological tissue is divided into three distinct events and modeled. 1) Initial tissue puncturing, which starts by soft tissue deformation and continues until a crack is formed in the tissue. Employing a viscoelastic model of fracture initiation we have predicted the maximum puncturing force and force-displacement response of a needle in contact with a tissue. 2) Tissue cutting, which follows the crack propagation in tissue and is predicted using a novel energy-based fracture model. The model takes account of the needle tip geometry and the tissue mechanical characteristics. 3) Friction between tissue and needle shaft is estimated during needle insertion and retraction using a needle-tissue friction model. Using a needle driving robot ex vivo experiments are performed on a porcine tissue sample to identify the model parameters and validate the analytical predictions offered by the models.
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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