Magnetically actuated dexterous tools for minimally invasive operation inside the brain
Bibliographic record
Abstract
Operating in the brain for deep-seated tumors or surgical targets for epilepsy is technically demanding and normally requires a large craniotomy with its attendant risk and morbidity. Neuroendoscopic surgery has the potential to reduce risk and morbidity by permitting surgical access through a small incision with burr hole and a narrow corridor through the brain. However, current endoscopic neurosurgical tools are straight and rigid and lack dexterity, hindering their adoption for neuroendoscopic procedures. We propose a class of robotic neurosurgical tools that have magnetically actuated wristed end effectors small enough to fit through a neuroendoscope working channel. The tools were less than 3.2 millimeters in overall diameter and contained embedded permanent magnets that allowed wireless actuation with magnetic fields. Three magnetic tools are presented: a two-degrees-of-freedom (DoFs) wristed gripper, a one-DoF pivoting scalpel, and a one-DoF twisted string-actuated forceps. This work evaluated the feasibility of these tools for completing minimally invasive neurosurgical resection and cutting tasks. Experimental tests on a silicone brain phantom showed that the tools could reach the ventricle area for simulated tumor removal and access a section of the corpus callosotomy for a simulated tissue-severing procedure in epilepsy treatment. Integration of the magnetic end effectors with a concentric tube robot as a hybrid steerable surgical robotic system enabled in vivo experiments on piglets. These experiments show that wireless magnetic tools could perform essential neurosurgical tasks, including gripping, cutting, and biopsy on living brain tissue, suggesting their potential for clinical applications.
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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 | 0.000 |
| 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) | 0.001 | 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".