Multiobjective Trajectory Tracking of a Flexible Tool During Robotic Percutaneous Nephrolithotomy
Bibliographic record
Abstract
Percutaneous Nephrolithotomy (PCNL) is the leading intervention for removing large or irregularly shaped kidney stones. It involves gaining access to the kidney through a small incision in the patient's back, through which a nephroscope is steered towards the stones. Despite decades of clinical prevalence, PCNL remains a complex procedure to learn and perform, sometimes requiring several attempts to gain kidney access, leading to a variety of complications. This letter proposes to use robotic assistance to steer a flexible nephroscope during PCNL to concurrently improve accuracy and reduce the risk of tissue damage. The nephroscope is modelled as a cantilever beam fixed to the robot's end-effector. Under the assumption that an optimal tooltip path exists, Non-dominated Sorting Genetic Algorithm-II is implemented to determine the end-effector position and orientation so that the tooltip follows the path while minimizing four objective functions, i.e., tissue compression, variations in the tool's strain energy, changes in end-effector position, and tooltip error. Data collected through experiments performed on ex-vivo porcine tissue show that the path tracking error was on average 2.03 mm. The results confirm the accuracy of the model in 2 dimensions and suggest that the multiobjective optimizer returned adequate solutions that minimized 4 different cost functions, altogether allowing the robot to effectively follow the predefined path.
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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 | 0.001 | 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".