Minimization of needle deflection in robot‐assisted percutaneous therapy
Bibliographic record
Abstract
BACKGROUND: Needle deflection and tissue deformation are two problems encountered during needle insertion into soft, non-homogeneous tissue. They affect the accuracy of needle placement, which in turn affects the effectiveness of needle-based therapies and biopsies. METHODS: In this study, a needle is inserted using a robot with two degrees of freedom. The needle is modelled as a flexible beam with clamped support at one end, and its deflection is estimated using online force/moment measurements at the needle base. To compensate for the needle deflection, the needle is axially rotated through 180 degrees . The needle deflection estimation data is used to find the insertion depths at which needle rotations are to be performed. RESULTS: A bevelled-tip needle was inserted into animal tissue. The needle deflection at the target was reduced by about 90%. It was observed that minimization of needle deflection reduced tissue deformation. The proposed method reduced needle deflection more than when needle insertion was performed with constant rotation. CONCLUSIONS: Estimating needle tip position using online force/moment measurement improves the accuracy of robot-assisted percutaneous procedures when imaging feedback is not available.
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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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".