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Contact the study team using the details below to take part. If there are no contact details below please ask your doctor in the first instance.

Contact Information:

Kavitha Vimalesvaran, MBBS MSc 020 7188 7188
kavitha.vimalesvaran@gstt.nhs.uk


Haris Shuaib, MSc 020 7188 7188
haris.shuaib@gstt.nhs.uk


Study Location:

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Be Part of Research - Trial Details - Assess the Clinical Effectiveness in AI Prioritising CT Heads

Assess the Clinical Effectiveness in AI Prioritising CT Heads

Recruiting

Open to: ALL

Age: 18.0 - N/A

Medical Conditions

Ischemic Stroke
Brain Injuries, Traumatic
Skull Fractures
Encephalomalacia
Intracranial Hemorrhages
Hemorrhage


This information is provided directly by researchers, and we recognise that it isn't always easy to understand. We are working with researchers to improve the accessibility of this information. In some summaries, you may come across links to external websites. These websites will have more information to help you better understand the study.


Non-Contrast Computed Tomography (NCCT) of the head is the most common imaging method used to assess patients attending the Emergency Department (ED) with a wide range of significant neurological presentations including trauma, stroke, seizure and reduced consciousness. Rapid review of the images supports clinical decision-making including treatment and onward referral.

Radiologists, those reporting scans, often have significant backlogs and are unable to prioritise abnormal images of patients with time critical abnormalities. Similarly, identification of normal scans would support patient turnover in ED with significant waits and pressure on resources.

To address this problem, Qure.AI has worked to develop the market approved qER algorithm, which is a software program that can analyse CT head to identify presence of abnormalities supporting workflow prioritisation.

This study will trial the software in 4 NHS hospitals across the UK to evaluate the ability of the software to reduce the turnaround time of reporting scans with abnormalities that need to be prioritised.

Start dates may differ between countries and research sites. The research team are responsible for keeping the information up-to-date.  

The recruitment start and end dates are as follows:

Mar 2024 Aug 2024

OBSERVATIONAL

Intervention Type : DEVICE
Intervention Description : Qure.ai's emergency room software solution qER (qER EU 2.0) is an AI medical device, developed by training a deep-learning algorithm using over 300,000 scans labelled by expert radiologists. qER has been shown to be accurate in identifying a range of abnormalities in NCCT head scans as well as prioritising them for urgent review and radiologist reporting. It is designated as a clinical support tool and, when used with original scans, can assist the clinician to improve efficiency, accuracy, and turnaround time in reading head CTs.

Intervention Arm Group : Post-implementation of qER;



You can take part if:



You may not be able to take part if:


This is in the inclusion criteria above


Below are the locations for where you can take part in the trial. Please note that not all sites may be open.

  • Oxford University Hospitals
    Oxford
    OX3 9DU
  • Guy's and St.Thomas Trusts
    London
    SE1 7EH
  • Northumbria Healthcare NHS Foundation Trust
    Northumberland
    NE27 0QJ
  • NHS Greater Glasgow and Clyde
    Glasgow


The study is sponsored by Guy's and St Thomas' NHS Foundation Trust and is in collaboration with Qure.ai; NHS Greater Glasgow and Clyde; Northumbria Healthcare NHS Foundation Trust; Oxford University Hospitals NHS Trust.




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Read full details for Trial ID: NCT06027411
Last updated 19 March 2024

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