Design and Comparison of Magnetically-Actuated Dexterous Forceps Instruments for Neuroendoscopy
Bibliographic record
Abstract
Robot-assisted minimally invasive surgical (MIS) techniques offer improved instrument precision and dexterity, reduced patient trauma and risk, and promise to lessen the skill gap among surgeons. These approaches are common in general surgery, urology, and gynecology. However, MIS techniques remain largely absent for surgical applications within narrow, confined workspaces, such as neuroendoscopy. The limitation stems from a lack of small yet dexterous robotic tools. In this work, we present the first instance of a surgical robot with a direct magnetically-driven end effector capable of being deployed through a standard neuroendoscopic working channel (3.2 mm outer diameter) and operate at the neuroventricular scale. We propose a physical model for the gripping performance of three unique end-effector magnetization profiles and mechanical designs. Rates of blocking force per external magnetic flux density magnitude were 0.309 N/T, 0.880 N/T, and 0.351 N/T for the three designs which matched the physical model's prediction within 14.9% error. The rate of gripper closure per external magnetic flux density had a mean percent error of 11.2% compared to the model. The robot's performance was qualitatively evaluated during a pineal region tumor resection on a tumor analogue in a silicone brain phantom. These results suggest that wireless magnetic actuation may be feasible for dexterously manipulating tissue during minimally invasive neurosurgical procedures.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".