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大脑小血管疾病病变细分方法:系统性审查

Jolene Phelps^1,2,3, Manpreet Singh^4,5,6,7, Cheryl R McCreary^1,8,9

¹Department of Clinical Neurosciences, University of Calgary, Calgary, Canada.

Cerebral circulation - cognition and behavior

|October 13, 2025

概括

自动机器学习工具可以对脑小血管疾病 (CSVD) 的脑病变进行细分,例如白质超强度 (WMH) 和脑微血 (CMB). 对于WMH存在高质量的工具,但对于其他CSVD类型的可用工具较少.

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) 涉及通过MRI检测到的大脑病变,影响大脑健康和痴呆风险.
这些病变的手动细分 (WMH,CMB,PVS,缺口,RSSI) 是耗时的研究.
正在开发自动机器学习方法,以实现高效准确的CSVD病变细分.

研究的目的:

系统地审查过去十年开发的新型自动化方法来细分CSVD病变.
评估这些方法在CSVD风险人群中的验证性.
确定公开可用的工具和代码,用于CSVD损伤细分.

主要方法:

在Web of Science和PubMed.com上进行系统的文献搜索.
对2764项获取的研究进行了选和全文审查.
根据细分的CSVD病变类型 (WMH,CMB,PVS,缺口,RSSI) 分类自动化方法的分类.

主要成果:

89项研究符合纳入标准,重点是CSVD病变的自动细分.
大多数方法都针对白质超强度 (59项研究),其次是脑微型血流 (23项研究).
对于周血管空间,缺口和最近的小皮下心脏病发作,可用的自动化工具较少.
代码或预训练模型可用于30项研究,主要用于WMH和CMB细分.

结论:

高质量的自动化工具可用于CSVD中的白质超强度细分.
需要更有效的自动化工具来对其他CSVD病变类型进行细分.
自动化细分工具的开发显示出对推进CSVD研究和痴呆症预防的前景.

关键词:

脑梗塞是因为脑梗塞.这种病是白血病araiosis.磁共振成像技术磁共振成像技术微型血液是一种微型血液.周血管空间周血管空间

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