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Area of Science:

  • Radiology
  • Medical Imaging
  • Artificial Intelligence in Medicine

Background:

  • Intracranial hemorrhage (ICH) detection on head computed tomography (HCT) scans is challenging in high-volume settings.
  • Teleradiology services are utilized in level I trauma centers.

Purpose of the Study:

  • To quantify additional ICHs detected by an AI algorithm.
  • To identify reasons for AI-related errors in ICH detection.

Main Methods:

  • Retrospective analysis of 4946 emergency non-contrast HCT scans from 18 hospitals.
  • Comparison of AI software (AIDOC) analysis with initial radiology reports (RR).
  • Blinded neuroradiologist review of discrepancies to confirm ICH and identify error causes.

Main Results:

  • AI detected an additional 29 ICH instances, increasing overall detection by 12.2%.
  • AI missed 12.4% of ICHs (often subarachnoid) and overcalled 1.9% (due to calcifications, artifacts, tumors).
  • Radiology reports missed 10.9% of ICHs (often outside working hours) and overcalled 0.2%.

Conclusions:

  • AI complements human expertise, enhancing ICH detection rates.
  • AI overcalled 1.9% of HCT scans, with common causes including calcifications and artifacts.