Optical Fibre-based Force Sensing Needle Driver for Minimally Invasive Surgery
Bibliographic record
Abstract
Minimally invasive surgery has been limited from its inception by insufficient haptic feedback to surgeons. The loss of haptic information threatens patients safety and results in longer operation times. To address this problem, various force sensing systems have been developed to provide information about tool–tissue interaction forces. However, the provided results for axial and grasping forces have been inaccurate in most of these studies due to considerable amount of error and uncertainty in their force acquisition method. Furthermore, sterilizability of the sensorized instruments plays a pivotal role in accurate measurement of forces inside a patient's body. Therefore, the objective of this thesis was to develop a sterilizable needle-driver type grasper using fibre Bragg gratings. In order to measure more accurate and reliable tool–tissue interaction forces, optical force sensors were integrated in the grasper jaw to measure axial and grasping forces directly at their exertion point on the tool tip. Two sets of sensor prototypes were developed to prove the feasibility of proposed concept. Implementation of this concept into a needle-driver instrument resulted in the final proposed model of the sensorized laparoscopic instrument. Fibre Bragg gratings were used for measuring forces due to their many advantages for this application such as small size, sterilizability and high sensitivity. Visual force feedback was provided for users based on the acquired real-time force data. Improvement and consideration points related to the current work were identified and potential areas to continue this project in the future are discussed.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".