CRONOS: A Continuum Robot for Neurovascular Surgery — Design, Development, and Feasibility Evaluation
Bibliographic record
Abstract
This work presents the design, development, and evaluation of a continuum robot for neurovascular surgery (CRONOS), addressing the lack of commercially available robots specifically designed for neurointervention. As the field evolves, assessing the feasibility of robot-assisted procedures, particularly for remote stroke treatment, is crucial. Designed for research purposes, CRONOS aims to explore the feasibility of such procedures. It features a trajectory planner, an open-source 3D-printed design, and independent control of three devices, providing four degrees of freedom. The system enables millimeter control and remote operation via a client–server network. To assess functionality, we simulated three stroke cases using a virtual system, categorizing data by operation mode: manual, robotic training, and robotic experiments. The robot was locally controlled via CAN bus during training and remotely operated in the experimental phase. Key performance metrics, including procedure success rate, device translation, and fluoroscopy time (FT), were compared to manual operations. The robotic system significantly reduced device translation, with robotic experiments showing a 52.2% decrease compared to manual mode. FT showed variability across modes, though manual procedures had the lowest values, with only a 9.7% difference from robotic experiments. These findings suggest that robot-assisted neurovascular interventions can enhance procedural control while maintaining comparable FT outcomes.
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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 | 0.002 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".