AI Assisted Reader Evaluation in Acute Computed Tomography (CT) Head Interpretation
AI-REACT
1 other identifier
observational
33
1 country
4
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.
Trial Health
Trial Health Score
Automated assessment based on enrollment pace, timeline, and geographic reach
participants targeted
Target at P25-P50 for all trials
Started Jun 2023
4 active sites
Health score is calculated from publicly available data and should be used for screening purposes only.
Trial Relationships
Click on a node to explore related trials.
Study Timeline
Key milestones and dates
Study Start
First participant enrolled
June 1, 2023
CompletedFirst Submitted
Initial submission to the registry
July 25, 2023
CompletedFirst Posted
Study publicly available on registry
August 30, 2023
CompletedPrimary Completion
Last participant's last visit for primary outcome
September 1, 2023
CompletedStudy Completion
Last participant's last visit for all outcomes
June 1, 2025
CompletedNovember 24, 2025
November 1, 2025
3 months
July 25, 2023
November 19, 2025
Conditions
Keywords
Outcome Measures
Primary Outcomes (8)
Reader performance: Sensitivity, specificity, comparative between with and without AI assistance.
Reader performance will be evaluated as sensitivity, specificity, with and without AI assistance.
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.
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.
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.
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.
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.
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).
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)
During 6 weeks, which is the period for reading or reviewing the cases/scans.
Study Arms (2)
Readers
30 readers will be recruited across four NHS trusts including ten general radiologists, fifteen emergency medicine clinicians, and five CT radiographers of varying seniority. Readers will interpret each scan first without, then with, the assistance of the AI tool, with an intervening 4-week washout period. Using a panel of neuroradiologists as ground truth, the stand-alone performance of qER will be assessed, and its impact on the readers' performance will be analysed as change in accuracy, mean review time per scan, and self-reported diagnostic confidence. Subgroup analyses will be performed by reader professional group, reader seniority, pathological finding, and neuroradiologist-rated difficulty.
Ground truthers
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. A difficulty score will be assigned to each scan by the ground truthers using a 5-point Likert scale.
Interventions
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.
Eligibility Criteria
Setting: Readers will be recruited from the following four hospital Trusts (secondary and tertiary level: * Guy's \& St Thomas NHS Foundation Trust * Northumbria Healthcare NHS Foundation Trust * NHS Greater Glasgow and Clyde * Oxford University Hospitals NHS Foundation Trust Participants: 30 volunteer participant readers will be selected from the following groups: * Emergency Medicine Consultants and Registrars (5 Consultant, 5 Registrar (ST3-6), 5 junior (F1-ST2) * General Radiologist Consultants and Registrars (5 Consultant, 5 Registrar) * 5 CT Radiographers
You may qualify if:
- Radiologists/Radiographers/ED clinicians who review CT head scans as part of their clinical practice
You may not qualify if:
- 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
Contact the study team to confirm eligibility.
Sponsors & Collaborators
Study Sites (4)
Guy's & St Thomas NHS Foundation Trust
London, London, SE1 7EH, United Kingdom
Oxford University Hospitals NHS Foundation Trust
Oxford, Oxfordshire, OX3 9DU, United Kingdom
NHS Greater Glasgow and Clyde
Glasgow, G12 0XH, United Kingdom
Northumbria Healthcare NHS Foundation Trust
Newcastle upon Tyne, NE27 0QJ, United Kingdom
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PMID: 38346874DERIVED
Related Links
- Richards M. Diagnostics: Recovery and Renewal - Report of the Independent Review of Diagnostic Services for NHS England. NHS England 2022.
- Royal College of Radiologists. Clinical radiology UK workforce census 2019 report. Royal College of Radiologists 2020.
- National Institute for Health and Care Excellence. Artificial intelligence for analysing CT brain scans. 2020.
MeSH Terms
Conditions
Condition Hierarchy (Ancestors)
Study Officials
- PRINCIPAL INVESTIGATOR
Alex Novak, MSc
National Health Services in the United Kingdom (NHS UK)
- PRINCIPAL INVESTIGATOR
Sarim Ather, PhD
National Health Services in the United Kingdom (NHS UK)
Study Design
- Study Type
- observational
- Observational Model
- COHORT
- Time Perspective
- RETROSPECTIVE
- Sponsor Type
- OTHER
- Responsible Party
- PRINCIPAL INVESTIGATOR
- PI Title
- Primary Investigator
Study Record Dates
First Submitted
July 25, 2023
First Posted
August 30, 2023
Study Start
June 1, 2023
Primary Completion
September 1, 2023
Study Completion
June 1, 2025
Last Updated
November 24, 2025
Record last verified: 2025-11