Head-to-Head Comparison of 2 Artificial Intelligence Computer-Aided Triage Solutions for Detecting Intracranial Hemorrhage on Noncontrast Head CT.
Journal:
AJNR. American journal of neuroradiology
Published Date:
Jan 30, 2026
Abstract
BACKGROUND AND PURPOSE: This study aims to provide a comprehensive comparison of the performance and reproducibility of 2 commercially available artificial intelligence (AI) software computer-aided triage and notification solutions, Vendor A (Aidoc) and Vendor B (Viz.ai), for the detection of intracranial hemorrhage (ICH) on noncontrast-enhanced head CT scans performed within a single academic institution. MATERIALS AND METHODS: The retrospective analysis was conducted on a large patient cohort from multiple health care settings within a single academic institution, utilizing standardized scanning protocols. Sensitivity, specificity, false-positive (FP), and false-negative (FN) rates were evaluated for both vendors. Outputs assessed included AI-generated case-level classification. RESULTS: Among 4081 scans, 595 were positive for ICH. Vendor A demonstrated a sensitivity of 94.4% and specificity of 97.4%, PPV of 77.7%, and NPV of 99.5%. Vendor B showed a sensitivity of 59.5% and specificity of 99.0%, PPV of 85.5%, and NPV of 96.2%. Vendor A had 20 FNs, which primarily involved subdural and intraparenchymal hemorrhages, and 97 FPs, which appear to be related to motion artifact. Vendor B had 145 FNs, largely comprising of subdural and subarachnoid hemorrhages, and 36 FPs, which appeared to be related to motion artifact and calcified or dense lesions. Concordantly, 18 cases were FNs and 11 cases were FPs for both AI solutions. CONCLUSIONS: The findings of this study provide valuable information for clinicians and health care institutions considering the implementation of AI software for computer-aided triage and notification in the detection of intracranial hemorrhage. The discussion encompasses the implications of the results, the importance of evaluating AI findings in context-especially in the absence of explainability tools, potential areas for improvement, and the relevance of standardized scanning protocols in ensuring the reliability of AI-based diagnostic tools in clinical practice.
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