Design and analysis of tactile optical sensor for endovascular surgery
Bibliographic record
Abstract
In this paper, design and Finite Element analysis of a new tactile optical sensor for the measurement of contact-pressure and tissue compliance in endovascular surgeries are presented. Using Micro-Electro-Mechanical-Systems (MEMS) technology, this sensor can be fabricated and integrated with the medical tools for endovascular surgeries such as Catheter tool. The designed sensor is capable of detecting the magnitude of the applied forces, the pressure distribution on contact objects, and also estimating the compliance of the contact tissue. The designed sensor is made of three layers, the upper layer is fabricated from monocrystalline silicon to form silicon membranes, the middle layer which is the supporting element is fabricated from both silicon and silicone rubber as a soft material and the lower layer is a supporting Plexiglas substrate to connect the designed sensor to the optical fibers. Simulation results show that for the given contact forces, the magnitude and the distribution of contacting tissues pressure along with tissue compliance can be determined. This sensor as proposed is a good candidate for batch micromachining, which is yet another commercial advantage for this design. Because of its less expensive cost, the surgeon can use it as a disposal part of the endovascular tools, requiring no re-sterilization and reducing the cost of surgery.
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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 | 0.000 |
| Scholarly communication | 0.000 | 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".