Improved Evaluation of Dynamic Mechanical Properties of Soft Materials With Applications to Minimally Invasive Surgery
Bibliographic record
Abstract
Minimally invasive surgery minimizes trauma to the patient, however, the loss of tactile feedback impedes the surgeon's ability to locate tumors among healthy tissue. In this paper, a resonance-based instrument to measure the stiffness, damping, and effective mass of a soft material such as biological tissue is presented. It was designed to be small yet has two natural frequencies below 100 Hz so that the effective mass of the tissue would not impact the determination of the stiffness. A state-space model was used to develop a fast and accurate method of extracting the tissue parameters by measuring the natural frequencies and the bandwidth at the first natural frequency. A fast and robust phased-locked-loop-based feedback system is described, which was used to measure the required frequencies. Simulations showed that the system was robust, while subjected to disturbances including hand tremor, tissue parameter variation, and preload. A prototype system showed that the instrument could accurately predict the stiffness, damping, and mass with an average error of 6%, 6%, and 7%, respectively. Experiments on a simulated tissue phantom showed the ability of the instrument to detect a tumour while it was stationary and in motion.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".