深度学习在脑小血管病影像学标志物中的研究进展

doi:10.3969/j.issn.1672-6731.2023.01.003

摘要/Abstract

摘要： 随着人工智能技术的飞速发展，特别是深度学习算法的应用，使脑小血管病典型影像学标志物的检测及量化评估速度增快、准确性提高。本文拟综述深度学习算法在脑微出血、脑白质高信号、扩大的血管周围间隙、腔隙、近期皮质下梗死及脑萎缩等脑小血管病影像学标志物中的研究进展，以为脑小血管病的精准医疗提供支持。

关键词: 大脑小血管疾病, 深度学习, 综述

Abstract: With the rapid development of artificial intelligence (AI) technology, especially the application of deep learning (DL), the detection and quantitative evaluation of typical imaging markers of small cerebral vascular disease (CSVD) has been accelerated and the accuracy has been improved. In recent years, it has attracted much attention in the field of medical imaging. This paper intends to summarize the research progress and problems of deep learning in the imaging markers of CSVD such as cerebral microbleeds (CMBs), white matter hyperintensities (WMH), enlarged perivascular space (EPVS), lacunes, recent small subcortical infarcts (RSSI) and cerebral atrophy, so as to provide support for the precise treatment of CSVD.

Key words: Cerebral small vessel diseases, Deep learning, Review

白雪冬, 张小雷, 夏爽. 深度学习在脑小血管病影像学标志物中的研究进展[J]. 中国现代神经疾病杂志, 2023, 23(1): 9-14.

BAI Xue-dong, ZHANG Xiao-lei, XIA Shuang. Progress of deep learning in cerebral small vessel disease imaging markers[J]. Chinese Journal of Contemporary Neurology and Neurosurgery, 2023, 23(1): 9-14.

http://journal11.magtechjournal.com/cjcnn/CN/Y2023/V23/I1/9

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