Enhancement of accuracy in shape sensing of surgical needles using optical frequency domain reflectometry in optical fibers
Bibliographic record
Abstract
We demonstrate a novel approach to enhance the precision of surgical needle shape tracking based on distributed strain sensing using optical frequency domain reflectometry (OFDR). The precision enhancement is provided by using optical fibers with high scattering properties. Shape tracking of surgical tools using strain sensing properties of optical fibers has seen increased attention in recent years. Most of the investigations made in this field use fiber Bragg gratings (FBG), which can be used as discrete or quasi-distributed strain sensors. By using a truly distributed sensing approach (OFDR), preliminary results show that the attainable accuracy is comparable to accuracies reported in the literature using FBG sensors for tracking applications (~1mm). We propose a technique that enhanced our accuracy by 47% using UV exposed fibers, which have higher light scattering compared to un-exposed standard single mode fibers. Improving the experimental setup will enhance the accuracy provided by shape tracking using OFDR and will contribute significantly to clinical applications.
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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) | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".