Robotic-assisted intracranial aneurysm treatment: 1 year follow-up imaging and clinical outcomes
Bibliographic record
Abstract
BACKGROUND: The use of robotics in medicine may enable increased technical accuracy, reduced procedural time and radiation exposure, and remote completion of procedures. We have previously described the first-in-human, robotic-assisted cerebral aneurysm treatment using the CorPath GRX Robotic System. In this report we discuss our early experiences and outcomes using this robotic device for endovascular treatment of intracranial aneurysms using stent-assisted coil embolization and flow diversion. METHODS: The patient and disease characteristics, procedural details, and follow-up imaging and clinical outcomes of consecutive patients undergoing robotically-assisted intracranial aneurysm embolization between November 2019 and February 2020 are presented. RESULTS: Six patients underwent robotically-assisted embolization of intracranial aneurysms. Four of the patients were treated with a neck-bridging stent (with or without coiling) and two patients were treated with a flow-diverting stent. Two patients were treated in the subacute period of subarachnoid hemorrhage and four patients were treated electively. All of the procedures could be completed robotically and there was no need for unplanned manual intervention. The technical success rate of the procedures was 100%. There was no morbidity or mortality associated with the procedures. One year follow-up imaging showed that four aneurysms were completely obliterated (Raymond-Roy Occlusion Classification (RROC) class I) and the remaining two were occluded with a residual neck (RROC class II). CONCLUSIONS: The Corpath GRX Robotic System demonstrated a precise control over the microcatheter, wire and stent during aneurysm treatment. Robotic neuro-procedures seem to be safe and effective and demonstrate stable occlusion results in the midterm follow-up.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".