PDF(Pubmed)
Abstract：
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. With the development of artificial intelligence technology based on deep learning, the extraction of high-dimensional features in imaging can assist doctors in clinical decision-making, and it has been widely used in brain function and mental disorders, and cardiovascular and cerebrovascular diseases. This paper summarizes the global research results in recent years and briefly describes the application of deep learning in evaluating CSVD signs in MRI imaging, including recent small subcortical infarcts, lacunes of presumed vascular origin, vascular white matter hyperintensity, enlarged perivascular spaces, cerebral microbleeds, brain atrophy, cortical superficial siderosis, and cortical cerebral microinfarct.
摘要：
脑小血管病(CSVD)是脑卒中的重要病因,认知障碍，和其他疾病，早期定量评价能显著改善患者预后。磁共振成像(MRI)是评价其发生、发展的重要方法,发展,CSVD的严重程度。然而,诊断过程缺乏定量评估标准，受经验限制，这很容易导致漏诊和误诊。随着基于深度学习的人工智能技术的发展,影像高维特征的提取可以辅助医生的临床决策,它已被广泛用于大脑功能和精神障碍，和心脑血管疾病。本文总结了近年来全球的研究成果,简述了深度学习在磁共振成像CSVD征象评估中的应用,包括最近的小皮质下梗塞,推测血管起源的空洞，血管白质高强度，血管周围间隙增大,脑微出血,脑萎缩,皮质浅表铁质沉着症，和皮质脑微梗死。
