ArticleAJNR. American journal of neuroradiology2025
World's First Real-Time Artificial Intelligence-Assisted Mechanical Thrombectomy for Acute Ischemic Stroke.
Article in AJNR. American journal of neuroradiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- Clinical Implementation and Performance of Real-Time AI System for Device Tracking in Neurointervention: First U.S. Evaluation of Neuro-Vascular Assist.Clinical neuroradiology · 2026Article
- Real-Time AI-Generated Guidewire-Out-of-View Notifications in Diagnostic Cerebral Angiography: Higher Observed Rates Among Trainees.Clinical neuroradiology · 2026Article
- Initial Multicenter Evaluation of a Novel Anchoring Exchange Device: AI-based Quantitative Distal Tip-Motion Analysis.Clinical neuroradiology · 2026Article
- Real-Time Artificial Intelligence-Based Detection of Flow-Directed Microcatheter Tip Markers.Journal of neuroendovascular therapy · 2026Article
- World's First Artificial Intelligence-Based Evaluation of Rist Catheter Stability in Transradial Procedures: A Feasibility Study.Journal of neuroendovascular therapy · 2025Article
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6 authors.
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Abstract
Several recent reports have discussed real-time artificial intelligence (AI) assistance in elective neuroendovascular procedures such as carotid artery stent placement, coil embolization for cerebral aneurysms, and liquid embolization. We present our preliminary experience with real-time AI-assisted mechanical thrombectomy (MT) for acute ischemic stroke (AIS) under local anesthesia. A real-time deep learning-based AI software (Neuro-Vascular Assist, iMed Technologies) was used during MT for 16 consecutive patients with AIS. The AI system provided real-time notifications when the guiding catheter (GC) moved out of the fluoroscopic image. The efficacy, accuracy, and safety of the notifications were evaluated by using video recordings. The AI system functioned properly in all cases. The mean number of notifications per case for the GC moving out of view was 8.1. The overall precision and recall of AI notifications were 97% and 99%, respectively. Of the 126 true-positive AI notifications, 25 (20%) prompted operators to reposition the GC within 10 seconds when it became unobservable in both frontal and lateral imaging planes. This response indicates the potential effectiveness of the AI system. No adverse events or delays in procedures due to the AI system occurred. In this preliminary study, the software was sufficiently accurate and safe for MT procedures, suggesting its usefulness. To the best of our knowledge, this is one of the first studies to report using a real-time AI system for MT in patients with AIS as an emergent procedure. Large-scale studies are warranted to validate its impact on procedural workflow and clinical outcomes.
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