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.
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See this in plain English?
AI-rewrites the medical criteria so a patient or caregiver can understand them. Always confirm with the trial site.
Bring these to your next appointment. They're a starting point for a shared conversation — not a sign you qualify or a recommendation to enrol.
Generated to help you prepare — always confirm anything about your own eligibility and care with the study team and your doctor.
The trial coordinator is the person who runs the study day to day. These cover the practical side — logistics, costs, and what taking part would actually mean for your life. The study team confirms whether you meet the criteria; these are questions to ask, not a sign you qualify.
A starting point for the conversation — always confirm anything about your own eligibility, costs, and care with the study team and your doctor.
Reader performance: Sensitivity, specificity, comparative between with and without AI assistance.
Timeframe: During 6 weeks, which is the period for reading or reviewing the cases/scans.
Reader performance: Positive and negative predictive value, comparative between with and without AI assistance.
Timeframe: During 6 weeks, which is the period for reading or reviewing the cases/scans.
Reader performance: Area Under Receiver Operating Characteristic Curve (AUROC), comparative between with and without AI assistance.
Timeframe: During 6 weeks, which is the period for reading or reviewing the cases/scans.
Reader speed: Mean time taken to review a scan, with versus without AI assistance.
Timeframe: During 6 weeks, which is the period for reading or reviewing the cases/scans.
Reader confidence: Self-reported diagnostic confidence on a 10 point visual analogue scale, with vs without AI assistance.
Timeframe: During 6 weeks, which is the period for reading or reviewing the cases/scans.
qER (AI algorithm) performance: Sensitivity and specificity
Timeframe: During 6 weeks, which is the period for reading or reviewing the cases/scans.
qER (AI algorithm) performance: Positive and negative predictive value.
Timeframe: During 6 weeks, which is the period for reading or reviewing the cases/scans.
qER (AI algorithm) performance: Area Under Receiver Operating Characteristic Curve (AUROC).
Timeframe: During 6 weeks, which is the period for reading or reviewing the cases/scans.