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Differentiating Between Brain Hemorrhage and Contrast

Artificial Intelligence for Differentiating Between Brain Hemorrhage and Contrast Extravasation After Mechanical Revascularization in Acute Ischemic Stroke

Status
UNKNOWN
Phases
Unknown
Study type
Observational
Source
ClinicalTrials.gov
Registry ID
NCT06032819
Enrollment
500
Registered
2023-09-13
Start date
2023-09-30
Completion date
2024-12-31
Last updated
2023-09-13

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Ischemic Stroke

Keywords

Hemorrhage, Contrast Media Extravasation, Artificial Intelligence, Mechanical Revascularisation

Brief 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. 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.

Interventions

None listed

Sponsors

Hong Kong University of Science and Technology
CollaboratorOTHER
First Affiliated Hospital, Sun Yat-Sen University
CollaboratorOTHER
The Seventh Affiliated Hospital of Sun Yat-sen University
CollaboratorOTHER
Peking University Shenzhen Hospital
CollaboratorOTHER
The Third Affiliated Hospital of Guangzhou Medical University
CollaboratorOTHER
The Third Affiliated Hospital of Southern Medical University
CollaboratorOTHER_GOV
Shenshan Medical Center, Memorial Hospital of Sun Yat-sen University
CollaboratorUNKNOWN
Shantou Central Hospital
CollaboratorOTHER
Dongguan People's Hospital
CollaboratorOTHER_GOV
The First People's Hospital of Qinzhou
CollaboratorUNKNOWN
Guangdong 999 Brain Hospital
CollaboratorOTHER
Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
Lead SponsorOTHER

Study design

Observational model
CASE_CONTROL
Time perspective
RETROSPECTIVE

Eligibility

Sex/Gender
ALL
Age
18 Years to 100 Years
Healthy volunteers
No

Inclusion criteria

(1) 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.

Exclusion criteria

(1) 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.

Design outcomes

Primary

MeasureTime frameDescription
Develop a deep learning model to differentiate brain hemorrhage from contrast agent extravasation, and evaluate the model performance and generalization ability2024-12The accuracy, sensitivity, specificity, precision, and recall of the model will be calculated, and confusion matrix will be display.

Contacts

Primary ContactMeiwei Chen
chenmw7@mail.sysu.edu.cn+86 18898534109

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Feb 4, 2026