Impact of Image Latency and Frame Rate on Simulated Remote Robotic-Assisted Neurovascular Procedures
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The implementation of remote procedures represents the ultimate goal of the robotic development in the neurovascular field. Studies from remote cardiac interventions established a maximum latency threshold of 400 ms, however, no data are available for neurovascular procedures. The aim of this study was to define the maximum acceptable latency and minimum refreshment frame rate (RFR) for neuroendovascular procedures in a simulated remote setting. MATERIALS AND METHODS: Using a virtual simulator and an endovascular robotic arm, 7 operators performed 8 simulated aneurysm and stroke treatment interventions (4 manually and 4 robotic-assisted), during which video display of the intervention was randomly altered with different latencies (100, 250, 450, 600, 800 ms) and RFR (10, 15, 25, 30 frames per second [fps]). Operators rated the acceptability of each latency and RFR by using a modified acceptability score (mAS) and an independent observer recorded the number of dangerous uncontrolled movement (DUMs). RESULTS: Maximum acceptable latency (defined as a minimum mAS of 85%) was defined at 100 ms for manually performed procedures and at 250 ms by using robotic-assistance, whereas minimum acceptable RFR was defined at 15 fps. A total of 55 intracranial DUMs were recorded, most of which occurred at latencies ≥450 ms (49/51) and with RFRs of 10 fps (4/4). Time intervals were shorter for manual procedures, although not significantly, and for experienced operators. CONCLUSIONS: Latency during simulated neurovascular interventions influences operator performance, judgment, and confidence and maximum thresholds (250 ms) seem to be lower than those previously reported from remote cardiac interventions. In this experimental setting, RFR seemed to have a lower impact in terms of acceptance rates. Latency and RFR represent relevant parameters to define and monitor in remote environments to maximize safety.
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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 | 0.017 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".