Using AI to Improve CT Angiography for Better Treatment of Large Vessel Blockage in Acute Ischemic Stroke Patients

What this Trial Is About

Acute ischemic stroke caused by a blockage in a large blood vessel inside the brain is a serious condition that often leads to disability and death. This trial investigates whether using an artificial intelligence AI algorithm to improve the visualization of these blockages on CT angiography scans can help doctors perform endovascular thrombectomy treatments more quickly and accurately. The study is designed as a stepped-wedge cluster-randomized trial to evaluate the impact of integrating AI into stroke care workflows. Participants will receive one of two approaches standard care without AI-assisted imaging or care with the AI algorithm providing automated reconstruction and segmentation of CT angiography scans to highlight the occluded vessel segments. Physicians will decide treatment based on clinical evaluation and images, with the AI images available during the intervention period. All patients deemed eligible will receive standard endovascular thrombectomy treatment per current guidelines. Throughout the study, researchers will measure the time to first pass of the clot during thrombectomy and other outcomes immediately after the procedure, such as flow restoration success, hemorrhage rates, and complications. Functional independence and mortality will be assessed up to 90 days post-treatment. The study aims to improve patient outcomes by using AI to assist doctors in navigating blocked vessels more effectively during stroke treatment.

AI-Driven CTA Reconstruction for Intracranial LVO