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

Active, not recruiting

• Conditions: Intracranial Hemorrhages; Cerebral Edema; Hydrocephalus; Cerebral Injury; Acute Ischemic Stroke; Cerebral Infarction

• Registration Number: NCT06018545
• Lead Sponsor: Oxford University Hospitals NHS Trust

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.

Detailed Description

Not available

Study Timeline

• Study Start: 2023-06-01
• Study First Submitted: 2023-07-25
• Study First Submitted QC: 2023-08-25
• Study First Posted: 2023-08-30
• Primary Completion: 2023-09-01
• Status Verified: 2024-06
• Last Update Submitted: 2024-06-20
• Last Update Posted: 2024-06-21
• Study Completion: 2025-06-01

Recruitment & Eligibility

• Status: ACTIVE_NOT_RECRUITING
• Sex: All
• Target Recruitment: 33

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

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
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.
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).
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 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 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.
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.
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)

Trial Locations

Locations (4)

Oxford University Hospitals NHS Foundation Trust; 🇬🇧 Oxford, Oxfordshire, United Kingdom

NHS Greater Glasgow and Clyde; 🇬🇧 Glasgow, United Kingdom

Guy's & St Thomas NHS Foundation Trust; 🇬🇧 London, United Kingdom

Northumbria Healthcare NHS Foundation Trust; 🇬🇧 Newcastle Upon Tyne, United Kingdom