Intelligent Analysis and Clinical Validation of Cerebral Small Vessel Disease on Magnetic Resonance Imaging：A Multi-center Study
试验速览
- 阶段
- 不适用
- 状态
- 尚未招募
- 发起方
- 入组人数
- 1,000
- 试验地点
- 1
- 主要终点
- The performance of AI in lesion detection and diagnosis
研究概览
简要总结
Cerebral small vessel disease (CSVD) accounts for 20% of ischemic strokes and is the most common cause of vascular cognitive impairment. Early identification of CSVD is critical for early intervention and improve clinical outcomes. Magnetic resonance imaging (MRI) may represent as a sensitive and robust tool to detect early changes in brain subtle structures and functions. The study is to investigate the comprehensive evaluation by using AI in early diagnosis and management of CSVD.
详细描述
Cerebral small vessel disease (CSVD) is an important cause of stroke, cognitive impairment, and other diseases, and its early quantitative evaluation can significantly improve patient prognosis. Magnetic resonance imaging (MRI) is an important method to evaluate the occurrence, development, and severity of CSVD. However, the diagnostic process lacks quantitative evaluation criteria and is limited by experience, which may easily lead to missed diagnoses and misdiagnoses. Based on the current technical challenges, subject development and upgrade of knowledge, to avoid the occurrence of adverse medical accidents, simplify the diagnostic process, artificial intelligence（AI） has become the alternative method of choice, by constructing training deep learning model,which can assist doctors in clinical decision-making to improve diagnosis effectiveness of CSCD detection and diagnosis.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Prospective
入排标准
- 年龄范围
- 40 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •① Men and women age 40 years or older;
- •At least one vascular risk factor has been identified, including hypertension, diabetes, hyperlipidemia, coronary heart disease, and chronic kidney disease；
- •The patient performed two brain MRI Examinations simultaneously at a time interval of more than 6 months (≥6).
排除标准
- •① The patient had no vascular risk factors；
- •No clinical follow-up images;
- •There are significant motion artifacts in the image, which cannot meet the
结局指标
主要结局
The performance of AI in lesion detection and diagnosis
时间窗: 2 year
The performance of AI in lesion detection and diagnosis, including imaging quality, accuracy, sensitivity and specificity in lesion detection and imaging diagnosis.
次要结局
未报告次要终点
研究者
Xin Lou
Deputy Director of Department of Radiology
Chinese PLA General Hospital