Needle path control during insertion in soft tissue using a force-sensor-based deflection estimator
Bibliographic record
Abstract
Needle insertion is commonly used in procedures such as prostate brachytherapy or biopsy. In prostate brachytherapy, the success of the procedure depends on the accurate placement of needles in their pre-planned target location. In order to steer the needle towards a defined target, past research has used ultrasound-image-based needle localization for needle tip position feedback. Acquiring and processing of ultrasound images, however, significantly limits the control sampling rate. This work proposes a method for needle path prediction and control without the need for image feedback. The needle tip path obtained during insertion from a force-sensor-based deflection estimator is used to parameterize a kinematic bicycle model. The bicycle model is then used to predict the needle tip path and the ideal rotation depth for reaching a desired target. Experimental results show that the introduced method accurately predicts the needle tip path and the ideal rotation depth to guide the needle to a pre-defined target.
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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 | 0.000 |
| 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 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".