Design and validation of an MRI conditional surgical robot for neurosurgery with remote center-of-motion
Bibliographic record
Abstract
Brain tumors are among the most challenging diseases in neurosurgery. Robot-assisted minimally invasive surgery can enhance surgical accuracy. However, the trade-off between higher accuracy and a larger workspace, along with limited magnetic resonance imaging compatibility, restricts its further clinical use. This paper presents the design of a novel magnetic resonance imaging compatible hybrid structure surgical robot for neurosurgery, based on a remote center of motion mechanism. The design uses the center of rotation of the Hooke joint as the remote-center of motion point. A cable-driven system is used to arrange the motors outside the robot body, which enhances the magnetic resonance imaging compatibility. The inverse kinematics and Jacobian matrix of the mechanism were analyzed to facilitate the control. The performance of the mechanism was verified through positioning experiments, showing an average positioning accuracy is 0.344 ± 0.146 mm. The signal-to-noise ratio when robot was in motion reached 1.83%, meeting clinical requirement. The robot presented in this research demonstrated improved positioning accuracy, a larger workspace and enhanced magnetic resonance imaging compatibility compared to state-of-the-art research. These results indicated the potential of the robot for future 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".