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.
Study Type
OBSERVATIONAL
Enrollment
33
Two Consultant neuroradiologists will independently review the images to establish the 'ground truth' findings on the CT scans which will be used as the reference standard. In the case of disagreement, a third senior neuroradiologist's opinion will be sought for arbitration.
All 30 readers will review all 150 cases, in each of two study phases. The readers will provide their opinion on the presence or absence of some acute abnormalities, including intracranial haemorrhage, infarct, midline shift and fracture. They will provide a confidence in their diagnosis (10-point visual analogue scale), and a single click point to mark the location of each abnormality that they consider as being present. The time taken for each scan will be automatically recorded.
Guy's & St Thomas NHS Foundation Trust
London, London, United Kingdom
Oxford University Hospitals NHS Foundation Trust
Oxford, Oxfordshire, United Kingdom
NHS Greater Glasgow and Clyde
Glasgow, United Kingdom
Northumbria Healthcare NHS Foundation Trust
Newcastle upon Tyne, United Kingdom
Reader performance: Sensitivity, specificity, comparative between with and without AI assistance.
Reader performance will be evaluated as sensitivity, specificity, with and without AI assistance.
Time frame: 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.
Reader performance will be evaluated as Positive Predictive Value (PPV) and negative predictive value (NPV), with and without AI assistance.
Time frame: 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.
Reader performance will be evaluated as Area Under Receiver Operating Characteristic Curve (AUROC), with and without AI assistance.
Time frame: 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.
Reader speed will be evaluated as the man time taken to review a scan, using time unite of seconds.
Time frame: 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.
On the reading platform (RAIQC), one of the questions asks the level of confidence that the participant has in their diagnostic opinion. The question offers a scale of 1 to 10, where 1 is not confident, and 10 is highly confident.
Time frame: During 6 weeks, which is the period for reading or reviewing the cases/scans.
qER (AI algorithm) performance: Sensitivity and specificity
qER performance will be evaluated as sensitivity, specificity.
Time frame: During 6 weeks, which is the period for reading or reviewing the cases/scans.
qER (AI algorithm) performance: Positive and negative predictive value.
qER performance will be evaluated as Positive Predictive Value (PPV) and negative predictive value (NPV).
Time frame: 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).
qER performance will be evaluated as Area Under Receiver Operating Characteristic Curve (AUROC)
Time frame: During 6 weeks, which is the period for reading or reviewing the cases/scans.
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