  Cerebral small vessel disease (CSVD) is a major vascular contributor to
cognitive impairment in ageing, including dementias. Imaging remains the most
promising method for in vivo studies of CSVD. To replace the subjective and
laborious visual rating approaches, emerging studies have applied
state-of-the-art artificial intelligence to extract imaging biomarkers of CSVD
from MRI scans. We aimed to summarise published computer-aided methods to
examine three imaging biomarkers of CSVD, namely cerebral microbleeds (CMB),
dilated perivascular spaces (PVS), and lacunes of presumed vascular origin.
Seventy-one classical image processing, classical machine learning, and deep
learning studies were identified. CMB and PVS have been better studied,
compared to lacunes. While good performance metrics have been achieved in local
test datasets, there have not been generalisable pipelines validated in
different research or clinical cohorts. Transfer learning and weak supervision
techniques have been applied to accommodate the limitations in training data.
Future studies could consider pooling data from multiple sources to increase
diversity, and validating the performance of the methods using both image
processing metrics and associations with clinical measures.
