| Summary: | 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.
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