Experimental evaluation of robot-assisted tactile sensing for minimally invasive surgery
Bibliographic record
Abstract
The 10 mm incisions used in minimally invasive cancer surgery prevent direct manual palpation of internal organs, making intraoperative tumor localization difficult. A tactile sensing instrument (TSI), that uses a commercially available sensor to measure distributed pressure profiles along the contacting surface, has been developed to facilitate remote tissue palpation. The objective of this research was to assess the feasibility of using the TSI under robotic control to reliably locate underlying tumors. The performance of human and robot manipulation of the TSI to locate tumor phantoms embedded into ex vivo bovine livers was compared. An Augmented Hybrid Impedance Control scheme was implemented on a Mitsubishi PA10-7C robot to perform force/position control during the trials. The results showed that using the TSI under robotic control realized an average 35% decrease in the maximum forces applied, and more than a 50% increase in tumor detection accuracy when compared to manual manipulation of the same instrument. This demonstrates that tumor detection using tactile sensing is highly dependent on the consistent application of forces on the tactile sensing area and that robotic assistance can be of great benefit when trying to localize tumors during minimally invasive 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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".