Intracranial Hemorrhages, Acute Ischemic Stroke, Hydrocephalus, Cerebral Infarction, Cerebral Edema, Cerebral Injury
Conditions
Keywords
Radiology, Head tomography, Emergency medicine, Radiographer, Artificial intelligence
Brief summary
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.
Interventions
OTHERGround truthing
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.
OTHERReading
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.
Sponsors
Oxford University Hospitals NHS Trust
Study design
Observational model
COHORT
Time perspective
RETROSPECTIVE
Eligibility
Sex/Gender
ALL
Healthy volunteers
Yes
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
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Reader speed: Mean time taken to review a scan, with versus without AI assistance. | During 6 weeks, which is the period for reading or reviewing the cases/scans. | Reader speed will be evaluated as the man time taken to review a scan, using time unite of seconds. |
| Reader performance: Sensitivity, specificity, comparative between with and without AI assistance. | During 6 weeks, which is the period for reading or reviewing the cases/scans. | Reader performance will be evaluated as sensitivity, specificity, with and without AI assistance. |
| Reader performance: Positive and negative predictive value, comparative between with and without AI assistance. | During 6 weeks, which is the period for reading or reviewing the cases/scans. | Reader performance will be evaluated as Positive Predictive Value (PPV) and negative predictive value (NPV), with and without AI assistance. |
| Reader performance: Area Under Receiver Operating Characteristic Curve (AUROC), comparative between with and without AI assistance. | During 6 weeks, which is the period for reading or reviewing the cases/scans. | Reader performance will be evaluated as Area Under Receiver Operating Characteristic Curve (AUROC), with and without AI assistance. |
| Reader confidence: Self-reported diagnostic confidence on a 10 point visual analogue scale, with vs without AI assistance. | During 6 weeks, which is the period for reading or reviewing the cases/scans. | 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. |
| qER (AI algorithm) performance: Sensitivity and specificity | During 6 weeks, which is the period for reading or reviewing the cases/scans. | qER performance will be evaluated as sensitivity, specificity. |
| qER (AI algorithm) performance: Positive and negative predictive value. | During 6 weeks, which is the period for reading or reviewing the cases/scans. | qER performance will be evaluated as Positive Predictive Value (PPV) and negative predictive value (NPV). |
| qER (AI algorithm) performance: Area Under Receiver Operating Characteristic Curve (AUROC). | During 6 weeks, which is the period for reading or reviewing the cases/scans. | qER performance will be evaluated as Area Under Receiver Operating Characteristic Curve (AUROC) |
Countries
United Kingdom
Outcome results
None listed