AI-Driven CTA Reconstruction for Intracranial LVO

Acute ischemic stroke (AIS) caused by intracranial large vessel occlusion (LVO) in the anterior circulation significantly contributes to stroke-related disability and mortality. Recent randomized controlled trials have demonstrated substantial benefits of endovascular thrombectomy (EVT) when patients are appropriately triaged beforehand. However, accurately orienting the 'missed segment' during EVT remains challenging. Guide-wires often fail to navigate through the occlusion or are mistakenly directed into the small tranches or even cause vessel rupture. To address this clinical need, the investigators developed an artificial intelligence (AI) algorithm to automate the reconstruction of CT angiography (CTA), focusing on the occluded LVO segment. To evaluate the clinical utility of this AI algorithm, the investigators propose a prospective, stepped-wedge cluster-randomized study to determine whether integrating our AI algorithm into AIS care flow can reduce the time for first pass of the thrombus by improving the visualization of the occluded segment on CTA. Physicians will assess patient eligibility for thrombectomy, and all selected patients will receive standard care according to current guidelines. This approach is expected to enhance patient treatment outcomes for endovascular thrombectomy by leveraging readily available data.

NCT Number	NCT06645405
Lead Sponsor	Shanghai Jiao Tong University Affiliated Sixth People's Hospital
Conditions	Acute Ischemic Stroke, Artificial Intelligence (AI), Endovascular Thrombectomy, CT Angiography
Enrollment	174 participants
Start Date	2024-10-20
Primary Completion	2025-12-31 (estimated)
Study Completion	2025-12-31 (estimated)
Updated on ClinicalTrials.gov	2024-10-16