Ischemic StrokeNCT06032819
Differentiating Between Brain Hemorrhage and Contrast
An observational study of Ischemic Stroke, run by Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University.
Trial summary
The goal of this observational study is to use artificial intelligence to differentiate cerebral hemorrhage from contrast agent extravasation after mechanical revascularization in ischemic stroke. Read more about this study
- Who is it for?
- Ages 18 to 100 years
- Conditions the study is for
- Ischemic Stroke
- How many people
- 500 participants
- Where
- No sites listed yet
- Study dates
- Sep 2023to Dec 2024
- Phase
- Observational
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 18 to 100 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: From December 2017 to June 2023, patients with acute ischemic stroke or intracranial arterial stenosis who underwent mechanical vascularization at 10 centers will be retrospectively enrolled.
Who may be able to join
- patients underwent non-enhanced head CT after mechanical vascularization; (2) initial post-operative non-enhanced head CT was performed within 24 h after mechanical vascularization; and (3) intracranial hyper-intensity, which was defined as an objectively higher density than the surrounding grey or white matter in the parenchyma or higher density than cerebrospinal fluid in ventricles and cisterns, could be seen on the initial non-enhanced head CT after mechanical vascularization.
Who cannot join
- the follow-up time of non-enhanced head CT after mechanical vascularization was less than 24 h; (2) artifacts (e.g. metal artifacts or motion artifacts) affected the hyper-intensity in CT images; and (3) patients underwent craniotomy after mechanical vascularization, which made it difficult to identify the area of hyper-intensity.
Trial locations and contacts
No sites are listed yet.
About this study
Official title: Artificial Intelligence for Differentiating Between Brain Hemorrhage and Contrast Extravasation After Mechanical Revascularization in Acute Ischemic Stroke
The goal of this observational study is to use artificial intelligence to differentiate cerebral hemorrhage from contrast agent extravasation after mechanical revascularization in ischemic stroke.
The main question it aims to answer is: Whether artificial intelligence can help differentiate brain hemorrhage from contrast agent extravasation.
Patients with intracranial high-density lesions on CT scans within 24h after mechanical revascularization will be included. Expected to enroll 500 patients. The type of high-density lesion is determined according to dual-energy CT images or follow-up images. Patients will be divided into training group, validation and testing groups by stratified random sampling (6:2:2). After the images and the image labels are obtained, deep learning artificial intelligence will be used to learn the image characteristics and establish a diagnostic model, and the model performance and generalization ability will be evaluated.
- Conditions
- Ischemic Stroke
- Keywords
- Hemorrhage, Contrast Media Extravasation, Artificial Intelligence, Mechanical Revascularisation
- Medical subject headings
- Ischemic Stroke, Hemorrhage, Extravasation of Diagnostic and Therapeutic Materials
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| hemorrhage | The result of the Intracranial hyper-density on CT images is determined by dual-energy CT or follow-up images: hyper-density can be seen on the virtual non-contrasted image of dual-energy CT, or high density persist longer than 48 hours. | |
| simple contrast extravasation | There is no Intracranial hyper-density on the virtual non-contrast images of dual-energy CT, or the follow-up CT show that the hyper-density is absorbed within 48 hours. |
What is being measured
The primary outcome is the main question the study is designed to answer.
Develop a deep learning model to differentiate brain hemorrhage from contrast agent extravasation, and evaluate the model performance and generalization ability
The accuracy, sensitivity, specificity, precision, and recall of the model will be calculated, and confusion matrix will be display.
Measured: 2024-12
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted13 Sep 2023
- Study startSep 2023 (estimated)
- Primary completionJul 2024 (estimated)
- Study completionDec 2024 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Ischemic StrokeStudies 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
- Case-control
- Time perspective
- Retrospective
- Enrolment
- 500 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
- Sponsor type
- University, hospital or other
- Collaborators
- Hong Kong University of Science and Technology, First Affiliated Hospital, Sun Yat-Sen University, The Seventh Affiliated Hospital of Sun Yat-sen University, Peking University Shenzhen Hospital, The Third Affiliated Hospital of Guangzhou Medical University, The Third Affiliated Hospital of Southern Medical University, Shantou Central Hospital, Dongguan People's Hospital, The First People's Hospital of Qinzhou, Guangdong 999 Brain Hospital
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06032819
- Sponsor's protocol ID
- SYSKY-2023-865-01
- Status last verified
- Sep 2023
- First submitted
- 4 Sep 2023
- Last updated
- 13 Sep 2023
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided