Exploring robotic advances, applications, and challenges in neuroendovascular surgery: A scoping review of the CorPath GRX system
Bibliographic record
Abstract
BackgroundThe integration of robotics into neuroendovascular surgery has the potential to revolutionize the field by enhancing precision, reducing procedure-related risks, and improving patient outcomes. The CorPath GRX system represents a significant advancement in this domain. In this systematically conducted scoping review, we explore the current applications, advances, and challenges associated with robot-assisted neuroendovascular surgery.MethodsA comprehensive literature search was conducted across PubMed, Scopus, Embase, and Web of Science, covering publications from January 2015 to December 2024. Relevant data were extracted from studies involving human subjects and reporting the use of robotic guidance with the CorPath GRX system for neuroendovascular procedures for review and discussion.ResultsWe identified 14 studies with a total of 336 patients who underwent robot-assisted neuroendovascular procedures, including both diagnostic (48.2%) and therapeutic (51.8%) applications. Among the therapeutic interventions, 140 patients were treated for hemorrhagic pathology, including 128 procedures for unruptured intracranial aneurysms and 12 for ruptured aneurysms. These procedures primarily involved stent-assisted coiling or flow diversion therapy. Additionally, 34 patients were treated for ischemic pathology, primarily carotid artery stenosis, involving balloon angioplasty and stent placement. A tumor was addressed in 1 intervention via coil embolization of tumor feeders, showcasing the versatility of robot-assisted interventions. Technical success rates were high across the studies, with most procedures achieving 100% success. Manual conversions were for device malfunctions or anatomical challenges. Complications reports varied, with some studies detailing specific device malfunctions and others providing less specific accounts.ConclusionsOverall, the CorPath GRX system demonstrated high feasibility and safety for neuroendovascular procedures, indicating significant potential benefits, particularly if integrated into telemedicine practices. The CorPath GRX system shows promise in facilitating precise and safe neuroendovascular procedures. Current evidence highlights a wide range of benefits; however, further research is needed to investigate long-term outcomes and other potential areas of impact.
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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 | 0.010 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.020 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".