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Automated Intracranial Hemorrhage Detection: Real-World Experience in a Large Comprehensive Stroke Center.

Aakanksha Sriwastwa1, Michael Oswald1, Kara Weiss2

  • 1Department of Radiology, University of Cincinnati, Ohio.

Neurology. Clinical Practice
|March 30, 2026
PubMed
Summary

Automated tools show high accuracy for detecting intracranial hemorrhage (ICH) in real-world stroke patients. While effective for intra-axial hemorrhage, sensitivity for extra-axial hemorrhage is lower when isolated.

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Area of Science:

  • Neurology
  • Radiology
  • Medical Imaging

Background:

  • Automated intracranial hemorrhage (ICH) detection tools are increasingly used, but their real-world performance data is limited.
  • Evaluating these tools in clinical practice is crucial for understanding their diagnostic utility.

Purpose of the Study:

  • To assess the performance of an automated ICH detection software compared to board-certified radiologists.
  • To determine the accuracy, sensitivity, and specificity of the tool in a real-world stroke patient cohort.

Main Methods:

  • Retrospective analysis of noncontrast CT head images from code stroke patients (January 2022 - February 2023).
  • Comparison of automated software assessment against radiology reports as the gold standard.
  • Calculation of sensitivity, specificity, positive predictive value, and negative predictive value using SAS software.

Main Results:

  • The automated tool analyzed 1,402 of 1,434 scans (98%), excluding 32 nondiagnostic studies.
  • Overall accuracy was 81% for ICH detection (105/129 cases) and 99% for non-ICH cases (1,255/1,273 cases).
  • Sensitivity was high for intra-axial hemorrhage (IAH) at 94%, but lower for isolated extra-axial hemorrhage (EAH) at 43%.

Conclusions:

  • Automated ICH detection software demonstrates high overall accuracy in real-world clinical practice.
  • The tool excels in detecting intra-axial hemorrhage but shows limitations with isolated extra-axial hemorrhage.