Intracranial HemorrhagesAI-REACTNCT06018545
AI Assisted Reader Evaluation in Acute Computed Tomography (CT) Head Interpretation
An observational study of Intracranial Hemorrhages and Acute Ischemic Stroke, run by Oxford University Hospitals NHS Trust at 4 sites in United Kingdom.
Trial 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… Read more about this study
- Who is it for?
- Any ageHealthy volunteers can take part
- Conditions the study is for
- Intracranial Hemorrhages, Acute Ischemic Stroke, Hydrocephalus
- What is being tested?
- Ground truthing and Reading
- How many people
- 33 participants
- Where
- 4 sitesUnited Kingdom
- Study dates
- 1 Jun 2023to 1 Jun 2025
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages Any age
- All sexes
- Healthy volunteers accepted
- Child, Adult, Older adult
Study population: 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
Who may be able to join
- Radiologists/Radiographers/ED clinicians who review CT head scans as part of their clinical practice
Who cannot join
- 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
Trial locations and contacts
4 sites in 1 country.
United Kingdom4 sites
NHS Greater Glasgow and Clyde
Glasgow, G12 0XH
Guy's & St Thomas NHS Foundation Trust
London, London, SE1 7EH
Northumbria Healthcare NHS Foundation Trust
Newcastle upon Tyne, NE27 0QJ
Oxford University Hospitals NHS Foundation Trust
Oxford, Oxfordshire, OX3 9DU
About this study
Official title: AI Assisted Reader Evaluation in Acute CT Head Interpretation
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.
- Conditions
- Intracranial Hemorrhages, Acute Ischemic Stroke, Hydrocephalus, Cerebral Infarction, Cerebral Edema, Cerebral Injury
- Being tested
- Ground truthing, Reading
- Keywords
- Radiology, Head tomography, Emergency medicine, Radiographer, Artificial intelligence
- Medical subject headings
- Intracranial Hemorrhages, Ischemic Stroke, Hydrocephalus, Cerebral Infarction, Brain Edema, Brain Injuries
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Ground truthingOther
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.
ReadingOther
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.
| Study group | Role | What they receive |
|---|---|---|
| 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. Receives: Other: Reading | |
| 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. Receives: Other: Ground truthing |
What is being measured
The primary outcome is the main question the study is designed to answer.
Reader performance: Sensitivity, specificity, comparative between with and without AI assistance.
Reader performance will be evaluated as sensitivity, specificity, with and without AI assistance.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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).
Measured: 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)
Measured: During 6 weeks, which is the period for reading or reviewing the cases/scans.
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted30 Aug 2023
- Study start1 Jun 2023
- Primary completion1 Sep 2023
- Study completion1 Jun 2025
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (23)Papers the sponsor has linked to this study.
- Other trials for Intracranial HemorrhagesStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 33 participants (actual)
Sponsor and investigators
- Lead sponsor
- Oxford University Hospitals NHS Trust
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Alex Novak, Oxford University Hospitals NHS Trust
- Investigators
- Alex Novak, MSc, Principal investigator, National Health Services in the United Kingdom (NHS UK)Sarim Ather, PhD, Principal investigator, National Health Services in the United Kingdom (NHS UK)
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06018545
- Sponsor's protocol ID
- 310995 - A
- Acronym
- AI-REACT
- Status last verified
- Nov 2025
- First submitted
- 25 Jul 2023
- Last updated
- 24 Nov 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Publications
Juszczyk K, Ireland K, Thomas B, Kroon HM, Hollington P. Reduction in hospital admissions with an early computed tomography scan: results of an outpatient management protocol for uncomplicated acute diverticulitis. ANZ J Surg. 2019 Sep;89(9):1085-1090. doi: 10.1111/ans.15285. Epub 2019 Jun 17.
PubMed 31206250Chan J, Fan KS, Mak TLA, Loh SY, Ng SWY, Adapala R. Pre-Operative Imaging can Reduce Negative Appendectomy Rate in Acute Appendicitis. Ulster Med J. 2020 Jan;89(1):25-28. Epub 2020 Feb 18.
PubMed 32218624Greenhalgh R, Howlett DC, Drinkwater KJ. Royal College of Radiologists national audit evaluating the provision of imaging in the severely injured patient and compliance with national guidelines. Clin Radiol. 2020 Mar;75(3):224-231. doi: 10.1016/j.crad.2019.10.025. Epub 2019 Dec 19.
PubMed 31864722Lin E, Yuh EL. Computational Approaches for Acute Traumatic Brain Injury Image Recognition. Front Neurol. 2022 Mar 9;13:791816. doi: 10.3389/fneur.2022.791816. eCollection 2022.
PubMed 35370919Sheth SA, Giancardo L, Colasurdo M, Srinivasan VM, Niktabe A, Kan P. Machine learning and acute stroke imaging. J Neurointerv Surg. 2023 Feb;15(2):195-199. doi: 10.1136/neurintsurg-2021-018142. Epub 2022 May 25.
PubMed 35613840Yeo M, Tahayori B, Kok HK, Maingard J, Kutaiba N, Russell J, Thijs V, Jhamb A, Chandra RV, Brooks M, Barras CD, Asadi H. Review of deep learning algorithms for the automatic detection of intracranial hemorrhages on computed tomography head imaging. J Neurointerv Surg. 2021 Apr;13(4):369-378. doi: 10.1136/neurintsurg-2020-017099. Epub 2021 Jan 21.
PubMed 33479036Chilamkurthy S, Ghosh R, Tanamala S, Biviji M, Campeau NG, Venugopal VK, Mahajan V, Rao P, Warier P. Deep learning algorithms for detection of critical findings in head CT scans: a retrospective study. Lancet. 2018 Dec 1;392(10162):2388-2396. doi: 10.1016/S0140-6736(18)31645-3. Epub 2018 Oct 11.
PubMed 30318264Guo Y, He Y, Lyu J, Zhou Z, Yang D, Ma L, Tan HT, Chen C, Zhang W, Hu J, Han D, Ding G, Liu S, Qiao H, Xu F, Lou X, Dai Q. Deep learning with weak annotation from diagnosis reports for detection of multiple head disorders: a prospective, multicentre study. Lancet Digit Health. 2022 Aug;4(8):e584-e593. doi: 10.1016/S2589-7500(22)00090-5. Epub 2022 Jun 17.
PubMed 35725824Lee JY, Kim JS, Kim TY, Kim YS. Detection and classification of intracranial haemorrhage on CT images using a novel deep-learning algorithm. Sci Rep. 2020 Nov 25;10(1):20546. doi: 10.1038/s41598-020-77441-z.
PubMed 33239711Arbabshirani MR, Fornwalt BK, Mongelluzzo GJ, Suever JD, Geise BD, Patel AA, Moore GJ. Advanced machine learning in action: identification of intracranial hemorrhage on computed tomography scans of the head with clinical workflow integration. NPJ Digit Med. 2018 Apr 4;1:9. doi: 10.1038/s41746-017-0015-z. eCollection 2018.
PubMed 31304294Davis MA, Rao B, Cedeno PA, Saha A, Zohrabian VM. Machine Learning and Improved Quality Metrics in Acute Intracranial Hemorrhage by Noncontrast Computed Tomography. Curr Probl Diagn Radiol. 2022 Jul-Aug;51(4):556-561. doi: 10.1067/j.cpradiol.2020.10.007. Epub 2020 Nov 15.
PubMed 33243455Wardlaw JM, Mair G, von Kummer R, Williams MC, Li W, Storkey AJ, Trucco E, Liebeskind DS, Farrall A, Bath PM, White P. Accuracy of Automated Computer-Aided Diagnosis for Stroke Imaging: A Critical Evaluation of Current Evidence. Stroke. 2022 Jul;53(7):2393-2403. doi: 10.1161/STROKEAHA.121.036204. Epub 2022 Apr 20.
PubMed 35440170Finck T, Moosbauer J, Probst M, Schlaeger S, Schuberth M, Schinz D, Yigitsoy M, Byas S, Zimmer C, Pfister F, Wiestler B. Faster and Better: How Anomaly Detection Can Accelerate and Improve Reporting of Head Computed Tomography. Diagnostics (Basel). 2022 Feb 10;12(2):452. doi: 10.3390/diagnostics12020452.
PubMed 35204543Warman R, Warman A, Warman P, Degnan A, Blickman J, Chowdhary V, Dash D, Sangal R, Vadhan J, Bueso T, Windisch T, Neves G. Deep Learning System Boosts Radiologist Detection of Intracranial Hemorrhage. Cureus. 2022 Oct 13;14(10):e30264. doi: 10.7759/cureus.30264. eCollection 2022 Oct.
PubMed 36381767Dyer T, Chawda S, Alkilani R, Morgan TN, Hughes M, Rasalingham S. Validation of an artificial intelligence solution for acute triage and rule-out normal of non-contrast CT head scans. Neuroradiology. 2022 Apr;64(4):735-743. doi: 10.1007/s00234-021-02826-4. Epub 2021 Oct 8.
PubMed 34623478
Study at a glance
From the registry record, last updated by the sponsor on 24 Nov 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- AI Assisted Reader Evaluation in Acute CT Head Interpretation
- ClinicalTrials.gov ID
- NCT06018545
- Acronym / sponsor ID
- AI-REACT / 310995 - A
- Conditions
- Intracranial Hemorrhages, Acute Ischemic Stroke, Hydrocephalus, Cerebral Infarction, Cerebral Edema, Cerebral Injury
- Keywords
- Radiology, Head tomography, Emergency medicine, Radiographer, Artificial intelligence
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 33 participants (actual)
- Lead sponsor
- Oxford University Hospitals NHS TrustUniversity, hospital or other
- Study start
- 1 Jun 2023 (actual)
- Primary completion
- 1 Sep 2023 (actual)
- Study completion
- 1 Jun 2025 (actual)
- First posted
- 30 Aug 2023 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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 design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Readers | – | Other: Reading30 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 | – | Other: Ground truthingTwo 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. |
Who may qualify
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Child, Adult, Older adult
- Study population
- 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
Key inclusion criteria (1 listed)
- Radiologists/Radiographers/ED clinicians who review CT head scans as part of their clinical practice
Key exclusion criteria (3 listed)
- 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
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Ground truthingTwo 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. | Other | Ground truthers |
| ReadingAll 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. | Other | Readers |
Primary outcome
Reader performance: Sensitivity, specificity, comparative between with and without AI assistance.
Time frame: 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.
Time frame: 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.
Time frame: 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 speed: Mean time taken to review a scan, with versus without AI assistance.
Time frame: 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.
Time frame: 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
Time frame: 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.
Time frame: 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).
Time frame: 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)
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 24 Nov 2025.
- Background: PubMed 31206250Juszczyk K, Ireland K, Thomas B, Kroon HM, Hollington P. Reduction in hospital admissions with an early computed tomography scan: results of an outpatient management protocol for uncomplicated acute diverticulitis. ANZ J Surg. 2019 Sep;89(9):1085-1090. doi: 10.1111/ans.15285. Epub 2019 Jun 17.
- Background: PubMed 32218624Chan J, Fan KS, Mak TLA, Loh SY, Ng SWY, Adapala R. Pre-Operative Imaging can Reduce Negative Appendectomy Rate in Acute Appendicitis. Ulster Med J. 2020 Jan;89(1):25-28. Epub 2020 Feb 18.
- Background: PubMed 31864722Greenhalgh R, Howlett DC, Drinkwater KJ. Royal College of Radiologists national audit evaluating the provision of imaging in the severely injured patient and compliance with national guidelines. Clin Radiol. 2020 Mar;75(3):224-231. doi: 10.1016/j.crad.2019.10.025. Epub 2019 Dec 19.
- Background: PubMed 35370919Lin E, Yuh EL. Computational Approaches for Acute Traumatic Brain Injury Image Recognition. Front Neurol. 2022 Mar 9;13:791816. doi: 10.3389/fneur.2022.791816. eCollection 2022.
- Background: PubMed 35613840Sheth SA, Giancardo L, Colasurdo M, Srinivasan VM, Niktabe A, Kan P. Machine learning and acute stroke imaging. J Neurointerv Surg. 2023 Feb;15(2):195-199. doi: 10.1136/neurintsurg-2021-018142. Epub 2022 May 25.
- Background: PubMed 33479036Yeo M, Tahayori B, Kok HK, Maingard J, Kutaiba N, Russell J, Thijs V, Jhamb A, Chandra RV, Brooks M, Barras CD, Asadi H. Review of deep learning algorithms for the automatic detection of intracranial hemorrhages on computed tomography head imaging. J Neurointerv Surg. 2021 Apr;13(4):369-378. doi: 10.1136/neurintsurg-2020-017099. Epub 2021 Jan 21.
- Background: PubMed 30318264Chilamkurthy S, Ghosh R, Tanamala S, Biviji M, Campeau NG, Venugopal VK, Mahajan V, Rao P, Warier P. Deep learning algorithms for detection of critical findings in head CT scans: a retrospective study. Lancet. 2018 Dec 1;392(10162):2388-2396. doi: 10.1016/S0140-6736(18)31645-3. Epub 2018 Oct 11.
- Background: PubMed 35725824Guo Y, He Y, Lyu J, Zhou Z, Yang D, Ma L, Tan HT, Chen C, Zhang W, Hu J, Han D, Ding G, Liu S, Qiao H, Xu F, Lou X, Dai Q. Deep learning with weak annotation from diagnosis reports for detection of multiple head disorders: a prospective, multicentre study. Lancet Digit Health. 2022 Aug;4(8):e584-e593. doi: 10.1016/S2589-7500(22)00090-5. Epub 2022 Jun 17.
- Background: PubMed 33239711Lee JY, Kim JS, Kim TY, Kim YS. Detection and classification of intracranial haemorrhage on CT images using a novel deep-learning algorithm. Sci Rep. 2020 Nov 25;10(1):20546. doi: 10.1038/s41598-020-77441-z.
- Background: PubMed 31304294Arbabshirani MR, Fornwalt BK, Mongelluzzo GJ, Suever JD, Geise BD, Patel AA, Moore GJ. Advanced machine learning in action: identification of intracranial hemorrhage on computed tomography scans of the head with clinical workflow integration. NPJ Digit Med. 2018 Apr 4;1:9. doi: 10.1038/s41746-017-0015-z. eCollection 2018.
- Background: PubMed 33243455Davis MA, Rao B, Cedeno PA, Saha A, Zohrabian VM. Machine Learning and Improved Quality Metrics in Acute Intracranial Hemorrhage by Noncontrast Computed Tomography. Curr Probl Diagn Radiol. 2022 Jul-Aug;51(4):556-561. doi: 10.1067/j.cpradiol.2020.10.007. Epub 2020 Nov 15.
- Background: PubMed 35440170Wardlaw JM, Mair G, von Kummer R, Williams MC, Li W, Storkey AJ, Trucco E, Liebeskind DS, Farrall A, Bath PM, White P. Accuracy of Automated Computer-Aided Diagnosis for Stroke Imaging: A Critical Evaluation of Current Evidence. Stroke. 2022 Jul;53(7):2393-2403. doi: 10.1161/STROKEAHA.121.036204. Epub 2022 Apr 20.
- Richards M. Diagnostics: Recovery and Renewal - Report of the Independent Review of Diagnostic Services for NHS England. NHS England 2022.Link provided by the sponsor.
- Royal College of Radiologists. Clinical radiology UK workforce census 2019 report. Royal College of Radiologists 2020.Link provided by the sponsor.
- National Institute for Health and Care Excellence. Artificial intelligence for analysing CT brain scans. 2020.Link provided by the sponsor.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Alex Novak, MSc principal investigator
National Health Services in the United Kingdom (NHS UK)
Sarim Ather, PhD principal investigator
National Health Services in the United Kingdom (NHS UK)
4 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Intracranial Hemorrhages and Acute Ischemic Stroke, run by Oxford University Hospitals NHS Trust at 4 sites in United Kingdom.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 1 inclusion and 3 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 24 Nov 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 4 sites in United Kingdom. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Reader performance: Sensitivity, specificity, comparative between with and without AI assistance. (time frame: During 6 weeks, which is the period for reading or reviewing the cases/scans.). The study lists 8 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT06018545, which also lists 23 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 24 Nov 2025.