Three‐dimensional ultrasound‐guided robotic needle placement: an experimental evaluation
Bibliographic record
Abstract
BACKGROUND: Clinical use of image-guided needle placement robots has lagged behind laboratory-demonstrated robotic capability. Bridging this gap requires reliable and easy-to-use robotic systems. METHODS: Our system for image-guided needle placement requires only simple, low-cost components and minimal, entirely off-line calibration. It rapidly aligns needles to planned entry paths using 3D ultrasound (US) reconstructed from freehand 2D scans. We compare system accuracy against clinical standard manual needle placement. RESULTS: The US-guided robotic system is significantly more accurate than single manual insertions. When several manual withdrawals and reinsertions are allowed, accuracy becomes equivalent. In ex vivo experiments, robotic repeatability was 1.56 mm, compared to 3.19 and 4.63 mm for two sets of manual insertions. In an in vivo experiment with heartbeat and respiratory effects, robotic system accuracy was 5.5 mm. CONCLUSIONS: A 3D US-guided robot can eliminate error bias and reduce invasiveness (the number of insertions required) compared to manual needle insertion. Remaining future challenges include target motion compensation.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".