Ultrasound-Based Visual Servoing for Out-of-Plane Longitudinal Needle Tracking in Robot-Aided Percutaneous Nephrolithotomy
Bibliographic record
Abstract
Percutaneous nephrolithotomy (PCNL) is a minimally invasive procedure to remove large renal calculi through a small incision in the patient’s back. Ultrasound (US) imaging is commonly used to guide the needle to the kidney during this procedure. However, it requires an advanced level of dexterity to coordinate the US probe and the needle to keep the needle visible in the images at all times. Failure to maintain needle-probe alignment can result in inadvertent injury, bleeding, and other complications. The use of robotic assistance can alleviate the surgeon’s cognitive workload by enabling autonomous positioning of the US probe and accurate needle tracking. This paper presents a new US-guided visual servoing (VS) algorithm for needle tracking using longitudinal US images of a needle subjected to out-of-plane motion. The ultrasound probe can move in 4 degrees-of-freedom (DOF), that is, two translations and one rotation in the imaging plane, and one rotation out of the imaging plane. Unlike previously reported VS algorithms, 4-DOF tracking is achieved using only 2D-US images and without any additional position sensor or prior knowledge of the needle trajectory. The algorithm is validated extensively in three different experimental scenarios using a water tank, a tissue phantom, and ex-vivo porcine tissue. Results obtained from several trials confirm the algorithm’s ability to track the needle and maintain needle-probe alignment with an average error of 1.5 mm, despite an out-of-plane average needle deflection of 7 mm along a 60 mm insertion depth.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".