Abstract WMP63: Artificial Intelligence Assessment Of Non-contrast Ct Increases Intracranial Occlusion Detection As Compared With Ct-angiograhy Readings

Abstract

Artificial Intelligence (AI) can assist in vessel occlusion (VO) identification in acute stroke patients. We aim to investigate the impact of using an AI-based software for automated VO detection on non-contrast CT (AI-VO) as compared to CT-Aangiograpphy (CTA). Methods: From April to October 2020 all patients admitted with a suspected acute ischemic stroke underwent urgent non-contrast CT / CTA / CTP and were treated accordingly. Hypoperfusion areas defined as Tmax>6s on CTP (RAPID software), congruent with the clinical symptoms and a vascular territory, were considered VO (CTP-VO: ground truth). In addition, two experienced neuroradiologists blinded to CTP but not to clinical symptoms retrospectively evaluated CT and CTA to identify intracranial VO (CTA-VO). AI-VO was automatically determined by an AI-based software (Methinks). Results: Of the 338 patients included, 157 (46.5%) showed a CTP-VO (median Tmax>6s: 73[29-127]ml). Overall sensitivity to detect CTP-VO was 50.3% for CTA-VO and 66.9% for AI-VO; specificity was 97.8% for CTA-VO and 86.2% for AI-VO. EVT was performed in 103 patients (EVT-VO: 65.6% of CTP-VO; Tmax>6s: 102[63-160]ml); sensitivity to detect EVT-VO was 69% for CTA-VO and 79.6% for AI-VO; specificity was 95.3% for CTA-VO and 79.6% for AI-VO. The probability to detect a CTP-VO was higher with AI than with CTA for distal occlusions (figure). Accordingly, AI-VO sensitivity was higher than CTA-VO for angiographically confirmed M2/M3-MCA occlusions (80.7% vs 34.6%; p=0.002) but not for M1-MCA/ICA occlusions (82.1% Vs 88.1%;p=0.467). Conclusion: AI-assisted vessel occlusion identification on non-contrast CT may be a useful tool in acute stroke evaluation, especially for distal VO identification, potentially increasing endovascular treatment in these cases.