AI Assisted Reader Evaluation in Acute Computed Tomography (CT) Head Interpretation

AI Assisted Reader Evaluation in Acute CT Head Interpretation

This study has been added as a sub study to the Simulation Training for Emergency Department Imaging 2 study (ClinicalTrials.gov ID NCT05427838).

The purpose of the study is to assess the impact of an Artificial Intelligence (AI) tool called qER 2.0 EU on the performance of readers, including general radiologists, emergency medicine clinicians, and radiographers, in interpreting non-contrast CT head scans. The study aims to evaluate the changes in accuracy, review time, and diagnostic confidence when using the AI tool. It also seeks to provide evidence on the diagnostic performance of the AI tool and its potential to improve efficiency and patient care in the context of the National Health Service (NHS). The study will use a dataset of 150 CT head scans, including both control cases and abnormal cases with specific abnormalities. The results of this study will inform larger follow-up studies in real-life Emergency Department (ED) settings.

Inclusion Criteria:

• Radiologists/Radiographers/ED clinicians who review CT head scans as part of their clinical practice

Exclusion Criteria:

• Neuroradiologists.
• Non-radiologist groups: Clinicians with previous formal postgraduate CT reporting training
• Emergency Medicine group: Clinicians with previous career in radiology/neurosurgery to registrar level