E-080 Impact of image latency and frame rate on simulated remote robotic-assisted neurovascular procedures
Bibliographic record
Abstract
Background and Aim In the past 5 years, our group and others have shown the feasibility and safety of robotic-assisted neurointerventional procedures. Remote robotics for treatment of acute ischemic stroke in remote communities holds great promise, however many challenges, such as connectivity parameters, will need to be evaluated in anticipation of making remote stroke treatment a reality. The goal of this study was to determine the maximum acceptable latency and minimum refreshment frame rate (RFR) for neuroendovascular procedures in a double-blinded simulated remote setting. Methods Using an neuroendovascular robotic system and a virtual simulator, 7 operators performed 8 simulated aneurysm and stroke procedures both manually and with robotic-assistance, during which the interventional imaging was randomly altered with different latencies (between 100 - 800 ms) and RFR (between 10- 30 frames/s). Operators rated their experience performing the procedure for each latency and RFR using a modified Acceptability Score (AS) and dangerous uncontrolled maneuvers (DUMs) were evaluated by an independent observer. Results Maximum acceptable latency was defined at 100 ms for manually performed procedures and at 250 ms for robotic-assisted procedures, whereas minimum acceptable RFR was defined at 15 fps. A total of 74 DUMs were recorded, most of which occurred at latencies ≥450ms and with RFRs of 10fps. Conclusion Latency during simulated neurovascular interventions influences operator performance, judgement and confidence and maximum thresholds seem to be lower than those previously reported from remote cardiac interventions. Latency and RFR are important parameters to define and monitor during remote environments to maximize safety. Disclosures N. Cancelliere: None. A. Consoli: None. G. Charbonnier: None. T. Baena Moura: None. K. Gaber: None. E. Liu: None. V. Mendes Pereira: None.
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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 | 0.008 |
| 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) | 0.007 | 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".