Artificial Intelligence for Differentiating Between Brain Hemorrhage and Contrast Extravasation After Mechanical Revascularization in Acute Ischemic Stroke
试验速览
- 阶段
- 不适用
- 状态
- 尚未招募
- 入组人数
- 500
- 主要终点
- Develop a deep learning model to differentiate brain hemorrhage from contrast agent extravasation, and evaluate the model performance and generalization ability
研究概览
简要总结
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.
研究设计
- 研究类型
- Observational
- 观察模型
- Case Control
- 时间视角
- Retrospective
入排标准
- 年龄范围
- 18 Years 至 100 Years（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •(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.
排除标准
- •(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.
结局指标
主要结局
Develop a deep learning model to differentiate brain hemorrhage from contrast agent extravasation, and evaluate the model performance and generalization ability
时间窗: 2024-12
The accuracy, sensitivity, specificity, precision, and recall of the model will be calculated, and confusion matrix will be display.
次要结局
未报告次要终点