Towards Bimanual Operation of Magnetically Actuated Surgical Instruments
Bibliographic record
Abstract
Advances in magnetically actuated surgical instru-ments have reduced the size and increased the dexterity of tools for minimally invasive surgery. However, studies typically focus on evaluating the control of individual instruments during tool development, while few studies examined the deployment of multiple tools, despite the common need for bimanual operations in surgery. When more than one magnetically actuated instrument is positioned in close proximity and controlled with the same magnetic field source, uncoupled and independent control of multiple instruments becomes challenging due to the complex magnetic interactions from the magnetic instruments' interference and the external field actuation. The current paper proposes a novel bimanual operation approach, where one instrument is designed to be actuated using a spatially uniform magnetic field with static directions, and the other instrument is designed to be actuated with a rotating magnetic field. The proposed concept was evaluated with experiments and demonstrated with a simulated bimanual tissue cutting task, using an electromagnetic navigation system and two magnetic tools (a gripper and a pair of scissors) that satisfy the magnetic actuation design requirements. During bimanual operation, experiments showed a 19% gripping force drop of the gripper and less than 10% closing force drop of the scissors, resulting in 35 mN of scissors closing force for cutting.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".