Design and Evaluation of a Sterilizable Force Sensing Instrument for Minimally Invasive Surgery
Bibliographic record
Abstract
Although the inability to feel tool-tissue interaction forces during minimally invasive surgery (MIS) has been recognized as a significant difficulty encountered by surgeons during these procedures, existing sensorized technologies have not yet been approved for use in humans. The challenges of properly cleaning and sterilizing these instruments prevent them from being operating-room ready. The focus of this paper was to develop a sterilizable instrument that uses strain gauges, the most common force-sensing method, to measure the tool-tissue interaction forces in three degrees of freedom (DOFs) during MIS. A series of experiments is conducted to identify cables and connectors, as well as strain gauge adhesives and coatings to allow the instruments to successfully withstand autoclave sterilization. This resulted in the construction of a final prototype capable of measuring forces in three DOFs, which was able to withstand six sterilization cycles with good sensing performance (0.15-1.70 N accuracy, 0.02-1.20 N repeatability, and 0.11-1.05 N hysteresis depending on the measurement direction). This paper demonstrates that autoclave sterilization is possible for a strain-gauge instrumented device and can lead to more advances in the development of sensorized instruments for surgery and therapy.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".