Network Feasibility Study for Remote Robotic-assisted Neurovascular Procedures
Bibliographic record
Abstract
Ischemic stroke leads to over 12.2 million deaths each year and faces limited treatment accessdue to a shortage of surgeons and resources, even in advanced regions like Ontario. However, the advancement of communication technology offers a promising opportunity to explore remote robotic-assisted surgery as a potential solution. The key challenge lies in identifying the minimal network parameter requirements, such as latency, packet loss, jitter, and bandwidth, for safe remote neurointerventional procedures. The objective of this study is to define the minimum network requirements for remote robotic-assisted stroke surgery. To this end, a teleoperated robot has been developed for neurovascular procedures, and experiments have been conducted to evaluate network performance. The robot is teleoperated via TCP protocol and features a three devices control system with four degrees of freedom in joint space, thereby ensuring millimeter-level control and adaptability to commercial catheters used in neurointervention. The experiments involve performing the intracranial steps of four simulated stroke procedures under varying network conditions. Each procedure is repeated 15 times with different combinations of jitter, bandwidth, and packet loss. The study assesses the impact of these parameters on procedural performance through quantitative measurements and subjective evaluations by experienced physicians. Statistical analyses, such as the Keplan-Meier and Receiver Operating Characteristic evaluation, were performed to determine the optimal network thresholds required for successful remote robotic-assisted procedures. The minimal parameters found in this research are 0% packet loss, 50 ms jitter, and 200 MBps of bandwidth.
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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 | 0.006 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".