Computer-Aided Extraction of Select MRI Markers of Cerebral Small Vessel Disease: A Systematic Review

TL;DR

This systematic review summarizes AI-based methods for extracting MRI biomarkers of cerebral small vessel disease, highlighting current progress, limitations, and future directions for clinical application.

Contribution

It compiles and evaluates existing computer-aided imaging techniques for CSVD biomarkers, emphasizing the need for validated, generalizable pipelines across diverse cohorts.

Findings

Good performance on local test datasets

Limited validation across different cohorts

Transfer learning used to address data scarcity

Abstract

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.