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基于深度学习的多模式MRI标记器和大脑小血管疾病的临床风险因素的定量分析

Zhiliang Zhang¹, Zhongxiang Ding², Fenyang Chen²

¹School of Medical Imaging, Hangzhou Medical College, Hangzhou, People's Republic of China.

International journal of general medicine

|March 11, 2024

概括

深度学习准确地细分脑小血管疾病 (CSVD) 标志物. 年龄,血压和CSVD负载是缺口中风的关键风险因素,有助于诊断.

科学领域:

神经学神经学
医疗成像医学成像
人工智能的人工智能

背景情况:

大脑小血管疾病 (CSVD) 缺乏明显的临床症状.
多模式成像分析对于改善CSVD诊断至关重要.
深度学习为提取有价值的成像特征提供了潜力.

研究的目的:

应用深度学习来细分CSVD标记物在多模式MRI中.
与临床风险因素一起分析细分标记.
为了提高CSVD和缺口性中风的诊断准确度.

主要方法:

招募了211名缺口中风患者和83名对照人群.
利用V形瓶网络进行CSVD标记器的自动细分.
执行手动校正并计算损伤数量和体积.
在风险因素分析中使用二进制和有序后勤回归.

主要成果:

对于白质强度过高和最近的小皮质下心脏病发作细分的高度一致性 (DSC>0.90).
与缺口性心脏病发作相比,对大脑微型血液和周血管空间细分的提醒和精度更好.
年龄,缩血压和CSVD负载评分被确定为腔中风的独立风险因素 (P<0.05).
年龄与CSVD负载正相关;总胆固醇负相关 (P<0.05).

结论:

缺口性中风患者显示CSVD成像标志物增加.
年龄,缩血压和CSVD负载是缺口中风的独立风险因素.
多模式深度学习细分有效量化CSVD病变.

关键词:

大脑小血管疾病是大脑小血管疾病.临床风险因素临床风险因素深度学习是一种深度学习.图像分割图像细分图像细分图像标记器图像标记器缺口性中风缺口性中风量化量化量化的量化.

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