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BACKGROUND AND PURPOSE: Endovascular thrombectomy is an evidence-based treatment for large vessel occlusion (LVO) stroke. Commercially available artificial intelligence has been designed to detect the presence of an LVO on computed tomography angiogram (CTA). We compared Viz.ai-LVO (San Francisco, CA, USA) to CTA interpretation by board-certified neuroradiologists (NRs) in a large, integrated stroke network. METHODS: From January 2021 to December 2021, we compared Viz.ai detection of an internal carotid artery (ICA) or middle cerebral artery first segment (MCA-M1) occlusion to the gold standard of CTA interpretation by board-certified NRs for all code stroke CTAs. On a monthly basis, sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) were calculated. Trend analyses were conducted to evaluate for any improvement of LVO detection by the software over time. RESULTS: 3851 patients met study inclusion criteria, of whom 220 (5.7%) had an ICA or MCA-M1 occlusion per NR. Sensitivity and specificity were 78.2% (95% CI 72%-83%) and 97% (95% CI 96%-98%), respectively. PPV was 61% (95% CI 55%-67%), NPV 99% (95% CI 98%-99%), and accuracy was 95.9% (95% CI 95.3%-96.5%). Neither specificity or sensitivity improved over time in the trend analysis. CONCLUSIONS: Viz.ai-LVO has high specificity and moderately high sensitivity to detect an ICA or proximal MCA occlusion. The software has the potential to streamline code stroke workflows and may be particularly impactful when emergency access to NRs or vascular neurologists is limited.
Karamchandani et al. (Thu,) studied this question.