Abstract WP76: Artificial Intelligence CTA Vessel Density Mapping to Enhance Identification of Large Vessel Occlusions in Mobile Stroke Unit

Abstract

Background: Equipped with CT scanners capable of imaging the brain parenchyma and vasculature, Mobile Stroke Units (MSU) have the ability to image, diagnose and treat stroke patients in the prehospital setting. Automated CTA vessel density mapping could enhance frontline neurologist scan review in identifying large vessel occlusion (LVO), ensuring appropriate patient diagnosis and routing. Methods: We analyzed consecutive acute ischemic stroke patients undergoing CTA imaging in a regional Mobile Stroke Unit. Automated CTA vessel density mapping was performed in the field immediately after scan completion. CTA source images were wirelessly transferred to an off-site processing server (RAPID.Ai, IschemiaView) for artery reconstruction and color-coded density mapping, with blue, green, yellow, and red color shading indicating vessel density decreases of 70%-85%, 60%-75%, 45%-60%, and <45%. Results: Among all 16 patients, median processing time was 186 secs, and all images were available in time to aid clinical decision-making. Overall, automated processing yielded evaluable images in 94% (suboptimal contrast opacification precluded analysis of 1). Of the 15 diagnostically adequate exams, 100% (15/15) showed concordance for identification of anterior circulation occluded/abnormal vessel territories between automated CTA vessel density mapping and expert physician final CTA interpretation. Cases included true positives in 7, and true negatives in 8. Among true positives, CTA vessel density mapping identified the symptomatic occlusion in 6/6 and also correctly identified a severe cervical ICA stenosis unrelated to the clinical presentation in 1/1. Correctly detected intracranial occlusions included: ICA-17%. M1-17%, M1-M2 junction-17%, and M2-50%. Degree of vessel density diminution correlated with proximal-distal occlusion location. Conclusion: CTA vessel density mapping can feasibly and efficiently be conducted in Mobile Stroke Units and shows high accuracy in detection of large and medium intracranial vessel occlusions. Extension of mapping to the intracranial posterior circulation and algorithmic adjustment for proximal cervical stenoses/occlusions would further improve utility in aiding prehospital routing.