Needle path control during insertion in soft tissue using a force-sensor-based deflection estimator
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 it