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Radiological Investigation I: X-ray and CT01:30

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AI assisted reader evaluation in acute CT head interpretation (AI-REACT): protocol for a multireader multicase study.

Howell Fu1, Alex Novak2, Dennis Robert3

  • 1Oxford University Hospitals NHS Foundation Trust, Oxford, UK.

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|February 12, 2024
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Summary
This summary is machine-generated.

This study evaluates an AI tool's impact on healthcare staff interpreting non-contrast CT head scans (NCCTH). It assesses accuracy and review time changes when AI assists clinicians and radiographers in detecting abnormalities.

Keywords:
Clinical Decision-MakingComputed tomographyHead & neck imagingIntracerebral Hemorrhage

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Area of Science:

  • Radiology and Medical Imaging
  • Artificial Intelligence in Healthcare
  • Clinical Decision Support Systems

Background:

  • Non-contrast CT head scans (NCCTH) are crucial in emergency departments.
  • Artificial intelligence (AI) tools are emerging for NCCTH abnormality detection.
  • Limited evidence exists on AI's impact on non-radiologist healthcare staff reviewing NCCTH.

Purpose of the Study:

  • To assess the standalone performance of the qER EU 2.0 AI tool on NCCTH.
  • To evaluate the impact of AI assistance on the diagnostic accuracy and review time of various healthcare professionals interpreting NCCTH.
  • To analyze diagnostic confidence changes with AI tool utilization.

Main Methods:

  • Retrospective analysis of 150 NCCTH (60 control, 90 with abnormalities).
  • 30 healthcare professionals (radiologists, emergency clinicians, CT radiographers) reviewed scans with and without AI assistance.
  • Performance measured by accuracy (AUC), review time, and diagnostic confidence, compared against neuroradiologist ground truth.

Main Results:

  • The study will assess the standalone performance of the qER EU 2.0 AI tool.
  • It will quantify the change in accuracy (AUC), median review time, and self-reported diagnostic confidence for readers using the AI tool.
  • Subgroup analyses will explore variations by professional group, seniority, and pathology.

Conclusions:

  • The findings will provide evidence on the effectiveness of AI tools in supporting clinical decision-making for NCCTH interpretation.
  • This research will inform the integration of AI into routine clinical practice for emergency imaging.
  • The study aims to clarify the real-world impact of AI on diverse healthcare staff reviewing NCCTH.