Abstract TMP44: Artificial Intelligence-driven Automated Intracerebral Hemorrhage Volume Calculation Is More Accurate Than ABC/2

Introduction: Treatment of spontaneous intracerebral hemorrhage (ICH) requires rapid, accurate estimation of hemorrhage volume to determine appropriate patient care and guide prognosis. ICH volume estimation on Computed Tomography (CT) imaging using the ABC/2 formula is the clinical gold standard, however this method can be inaccurate, suffers from observer scoring variability, and takes time to make the measurement on a workstation. Semi-Autonomous Segmentation (SAS) is the gold standard for hemorrhage volume estimation, however it is not used clinically due to the increased time for analysis. Recently, artificial intelligence (AI) driven segmentation has been developed (Viz.ai, San Francisco, California) to automatically detect ICH and calculate hematoma volume. Objective: Our goal is to validate the accuracy of the Viz.ai ICH segmentation algorithm as a tool for determining hemorrhage volume by comparing its performance to both ABC/2 and SAS. Methods: Seventy head CTs positive for ICH were analyzed with SAS in 3D Slicer to determine ICH volume as the standard reference volume. The same CT scans were then analyzed using the ABC/2 method. Finally, scans were uploaded to Viz.ai for ICH volume analysis. Results: Compared against standard SAS, Viz.ai ICH volumes were more accurate than ABC/2 in 77% of cases. Average difference between Viz.ai ICH volume and SAS ICH volume was 4.9±4.2 mL (R2=0.98). Average difference between ABC/2 ICH volume and SAS ICH volume was 10.6±11.4 mL (R2=0.77). Conclusion: This study indicates that Viz.ai more accurately estimates ICH volume than ABC/2 over a broad range of hematoma volumes when compared to standard SAS, which when coupled with significantly faster analysis compared to SAS justifies the use of AI in ICH triage workflow.