Introducing notched flexible needles with increased deflection curvature in soft tissue
Bibliographic record
Abstract
Robotics-assisted needle steering can enhance the accuracy of needle-based medical interventions, especially when the designated target locations are obscured by obstacles. However, the steering techniques using standard needles are not capable of achieving high curvatures and cannot follow tightly curved paths inside tissue. In this work, we introduce a new notched steerable needle with improved curvature. The notched needle is developed by carving a series of small notches on a regular needle shaft. The notches decrease the needle's flexural rigidity and increase the maximum achievable curvature. First, we develop a model of the notched needle deflection inside soft tissue using the finite element method (FEM). The model relates the needle radius of curvature to the number of notches and their locations on the needle shaft. Next, the capability of the notched steerable needle in achieving high curvature and the model's accuracy in predicting needle curvature are validated by performing several needle insertion experiments on a tissue phantom. The results demonstrate that our newly developed needles can achieve a minimum radius of curvature of 198 mm, which is 67% better than standard needles, enabling future research in needle steering in tight spaces.
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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 | 0.000 | 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 | 0.000 | 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 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".