Innovative optical microsystem for static and dynamic tissue diagnosis in minimally invasive surgical operations
Bibliographic record
Abstract
During conventional surgical tasks, surgeons use their tactile perception in their finger tips to sense the degree of softness of biological tissues to identify tissue types and to feel for any abnormalities. However, in robotic-assisted surgical systems, surgeons are unable to sense this information because only surgical tools interact with tissues. In order to provide surgeons with such useful tactile perception, therefore, a tactile sensor is required that is capable of simultaneously measuring contact force and resulting tissue deformation. Accordingly, this paper discusses the design, prototyping, testing, and validation of an innovative tactile sensor that is capable of measuring the degree of softness of soft objects such as tissues under both static and dynamic loading conditions and which is also magnetic resonance compatible and electrically passive. These unique characteristics of the proposed sensor would also make it a practical choice for use in robotic-assisted surgical platforms. The prototype version of this sensor was developed by using optical micro-systems technology and, thus far, experimental test results performed on the prototyped sensor have validated its ability to measure the relative softness of artificial tissues.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".