Tactile Sensors for Minimally Invasive Surgery: A Review of the State-of-the-Art, Applications, and Perspectives
Bibliographic record
Abstract
Minimally invasive surgery has been one of the most significant evolutions in medicine. In this approach, the surgeon inserts specially-designed instruments through a small incision on the patient's skin into the body cavities, abdomen, veins or, arteries and performs the surgery on organs. As a major limitation, surgeons lose their natural tactile perception due to indirect touch on the organs. Since the loss of tactile perception compromises the ability of surgeons in tissue distinction and maneuvers, researchers have proposed different tactile sensors. This review is to provide researchers with a literature map for the state-of-the-art of tactile sensors in minimally invasive surgery, e.g. in robotic, laparoscopic, palpation, biopsy, heart ablation, and valvuloplasty. In this regard, the pertinent literature from the year 2000 on sensing principles, design requirements, and specifications were reviewed in this study. The survey showed that size, range, resolution, variation, electrical passivity, and magnetic-resonance-compatibility were the most critical specification to study for tactile sensors. Based on the results, some of the requirements, e.g., magnetic-resonance-compatibility and electrical passivity are of less generality and more application-dependent; however, size, resolution, and range specifications differ for various applications and are of utmost importance.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| 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".