Abstract

Introduction: Recent advances and the use of Artificial intelligence (AI) is changing the standards of care in stroke patients. AI software has a high sensitivity and specificity for identifying Large Vessel Occlusions (LVOs), but the accuracy for detecting Medium Vessel Occlusions (MeVOs) is limited. We investigated the performance of the combination of two different AI modules for the identification of MeVOs. Methods: 150 patients with MeVOs were identified on CTA, using CTP to help localize the lesions, from retrospective databases at the MUSC, Stanford, and Royal Melbourne. Additionally, 55 patients with LVOs on CTA were included. A neuroradiologist confirmed the occluded vessel on CTA for each patient. LVOs were defined as occlusions of the intracranial ICA or horizontal segment of the M1. MeVOs were defined at M2, M3, ACA (A2/A3), or PCA (P2/P3) occlusions. All cases were processed through Rapid LVO, the Rapid Vessel Density module (which assesses hemispheric differences in blood vessel density), and Rapid CTP (Ischemaview Inc., Menlo Park, CA), [AZ1] using automated hypoperfusion intensity ratio (HIR) on CT perfusion (CTP), which estimates collateral flow, differed in LVOs vs. MeVOs. Results: Rapid LVO alone identified occlusions in 41/150 (27.3%) of MeVO patients. Rapid Vessel Density maps identified an additional 38/150 (25.3%) patients with either a 20-25% vessel density reduction or 26-40% reduction. Together, these two modules identified 79/150 (52.6%) of MeVO patients. 54/55 (98.1%) of the LVOs were detected by the Rapid LVO module. The mean HIR was lower in MeVO vs. LVO patients (0.25 vs. 0.45, P-value < 0.001). Patients with M1 or ICA occlusions (LVOs) had average HIRs of 0.46 and 0.47 respectively. Patients with M2, M3, ACA, or PCA occlusions (MeVOs) had average HIRs of 0.30, 0.14, 0.22, and 0.20 respectively. Conclusion: MeVos detection was almost doubled with the use of the RapidAI Vessel Density in conjunction with the LVO module.

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