Estimation of Ventricular and Intracranial Hemorrhage Volumes and Midline Shift on an External Validation Data Set Using a Convolutional Neural Network Algorithm

BACKGROUND AND OBJECTIVES: Noncontrast head computed tomography is the mainstay imaging modality to guide the management of intracranial hemorrhage (ICH); however, manual measurements can be time-consuming. In our study, we evaluate the performance of an artificial intelligence (AI) machine learning algorithm, Viz ICH-Plus, to automatically quantify ICH and bilateral lateral ventricular (BLV) volumes as well as midline shift (MLS). METHODS: ICH patients considered for external ventricular drain with an initial noncontrast head computed tomography, and at least 1 follow-up scan within 48 hours was identified from a single center. Viz ICH-Plus estimations of ICH volume, BLV volume, and MLS were generated for each scan and compared with manually contoured and measured values. Median absolute errors and the ability of Viz ICH-Plus to detect clinically meaningful change from initial to follow-up scans (ICH volume growth ≥10 mL, BLV volume change ≥10 mL, or MLS increase ≥4 mm) were assessed. RESULTS: Thirty patients were included for a total of 78 scans. The median absolute error was 2.9 mL (IQR 1.2 to 5.8) for ICH, 5.3 mL (IQR 2.5 to 7.9) for BLV volume, and 1.1 mm (IQR 0.7 to 2.0) for MLS. The ability of Viz ICH-Plus to detect a clinically significant change between scans was robust with sensitivity, specificity, positive predictive value, negative predictive value, and overall accuracy of 91.7%, 92.6%, 84.6%, 96.2%, and 92.3%, respectively. CONCLUSION: The described Viz ICH-Plus algorithm performed moderately well at quantifying ICH, BLV volume, and MLS with satisfying spatial overlap of artificial intelligence and manual segmentations. The system demonstrated good predictive power when using predetermined thresholds to estimate clinically significant changes.