Clinical trial NCT06032819: Differentiating Between Brain Hemorrhage and Contrast
Official title: Artificial Intelligence for Differentiating Between Brain Hemorrhage and Contrast Extravasation After Mechanical Revascularization in Acute Ischemic Stroke
Study type: OBSERVATIONAL
Overall status: NOT_YET_RECRUITING
Conditions: Ischemic Stroke
Keywords: Hemorrhage
Contrast Media Extravasation
Artificial Intelligence
Mechanical Revascularisation
Lead sponsor: Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
Sponsor class: OTHER
Collaborators: name: Hong Kong University of Science and Technology
name: First Affiliated Hospital, Sun Yat-Sen University
name: The Seventh Affiliated Hospital of Sun Yat-sen University
name: Peking University Shenzhen Hospital
name: The Third Affiliated Hospital of Guangzhou Medical University
name: The Third Affiliated Hospital of Southern Medical University
name: Shenshan Medical Center, Memorial Hospital of Sun Yat-sen University
name: Shantou Central Hospital
name: Dongguan People's Hospital
name: The First People's Hospital of Qinzhou
name: Guangdong 999 Brain Hospital
First posted: 2023-09-13
Enrollment: 500
Sex: ALL
Minimum age (years): 18
Maximum age (years): 100
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: CASE_CONTROL
Time perspective: RETROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
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.

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.


Eligibility criteria: 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.

Primary outcomes:
measure: Develop a deep learning model to differentiate brain hemorrhage from contrast agent extravasation, and evaluate the model performance and generalization ability
timeFrame: 2024-12
Secondary outcomes:
Other outcomes:
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