Surface exploration using laparoscopic surgical instruments: The perception of surface roughness
Bibliographic record
Abstract
During laparoscopic surgery video images are used to guide the movements of the hand and instruments, and objects in the operating field often obscure these images. Thus, surgeons often rely heavily on tactile information (sense of touch) to help guide their movements. It is important to understand how tactile perception is affected when using laparoscopic instruments, since many surgical judgements are based on how a tissue 'feels' to the surgeon, particularly in situations where visual inputs are degraded. Twelve naïve participants used either their index finger or a laparoscopic instrument to explore sandpaper surfaces of various grits (60, 100, 150 and 220). These movements were generated with either vision or no vision. Participants were asked to estimate the roughness of the surfaces they explored. The normal and tangential forces of either the finger or instrument on the sandpaper surfaces were measured. Results showed that participants were able to judge the roughness of the sandpaper surfaces when using both the finger and the instrument. However, post hoc comparisons showed that perceptual judgements of surface texture were altered in the no vision condition compared to the vision condition. This was also the case when using the instrument, compared to the judgements provided when exploring with the finger. This highlights the importance of the completeness of the video images during laparoscopic surgery. More normal and tangential force was used when exploring the surfaces with the finger as opposed to the instrument. This was probably an attempt to increase the contact area of the fingertip to maximize tactile input. With the instrument, texture was probably sensed through vibrations of the instrument in the hand. Applications of the findings lie in the field of laparoscopic surgery simulation techniques and tactile perception.
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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 | 0.006 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".