Image-Based Optical-Fiber Force Sensor for Minimally Invasive Surgery with ex-vivo Validation
Bibliographic record
Abstract
During minimally invasive surgery, surgeons insert specially-designed instruments through a small incision into the patient’s body. Despite all the advantages of this procedure, surgeons do not have the natural force feedback in the surgery. Force feedback helps the surgeon to apply an appropriate force to avoid tissue damage. As a solution, this study was aimed at the ex-vivo validation of a proposed image-based optical force sensor with light intensity modulation principle. The sensor was to be integrated with conventional minimally invasive instruments and was working based on variable bending radius sensing principle. To this end, the sensor was integrated on the jaw of a custom-designed minimally invasive grasper and its performance was assessed ex-vivo . Furthermore, the light intensity measurement of this study was performed utilizing an image-based technique to avoid the complexities of using photodetectors. The sensor was calibrated using a rate-dependent learning-based support-vector-regression model, which showed an adjusted− R 2 of 94%. The results of the ex-vivo test on a freshly excised bovine muscle tissue showed fair agreement between sensor measurements and ground truth. Therefore, the proposed sensor was concluded as applicable for minimally invasive surgeries by comparing the minimum performance requirements of force sensors for surgical applications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".