Machine-learning method for localization of cerebral white matter hyperintensities in healthy adults based on retinal images. Issue 3 (3rd June 2021)

• Record Type: Journal Article
• Authors: Zee, Benny; Wong, Yanny; Lee, Jack; Fan, Yuhua; Zeng, Jinsheng; Lam, Bonnie; Wong, Adrian; Shi, Lin; Lee, Allen; Kwok, Chloe; Lai, Maria; Mok, Vincent; Lau, Alexander
• Is Part Of: Brain communications. Volume 3:Issue 3(2021)
• Publication Date: 2021-06-03
• Subjects: cerebral small vessel disease -- artificial intelligence -- stroke -- vascular dementia -- Alzheimer's disease; 616
• DOI: 10.1093/braincomms/fcab124
• Languages: English
• ISSNs: 2632-1297

Abstract

Abstract: Retinal vessels are known to be associated with various cardiovascular and cerebrovascular disease outcomes. Recent research has shown significant correlations between retinal characteristics and the presence of cerebral small vessel disease as measured by white matter hyperintensities from cerebral magnetic resonance imaging. Early detection of age-related white matter changes using retinal images is potentially helpful for population screening and allow early behavioural and lifestyle intervention. This study investigates the ability of the machine-learning method for the localization of brain white matter hyperintensities. All subjects were age 65 or above without any history of stroke and dementia and recruited from local community centres and community networks. Subjects with known retinal disease or disease influencing vessel structure in colour retina images were excluded. All subjects received MRI on the brain, and age-related white matter changes grading was determined from MRI as the primary endpoint. The presence of age-related white matter changes on each of the six brain regions was also studied. Retinal images were captured using a fundus camera, and the analysis was done based on a machine-learning approach. A total of 240 subjects are included in the study. The analysis of various brain regions included the left and right sides of frontal lobes, parietal–occipital lobes and basal ganglia. Our results suggested that data from both eyes are essential for detecting age-related white matter changes in the brain regions, but the retinal parameters useful for estimation of the probability of age-related white matter changes in each of the brain regions may differ for different locations. Using a classification and regression tree approach, we also found that at least three significant heterogeneous subgroups of subjects were identified to be essential for the localization of age-related white matter changes. Namely those with age-related white matter changes in the right frontal lobe, those without age-related white matter changes in the right frontal lobe but with age-related white matter changes in the left parietal–occipital lobe, and the rest of the subjects. Outcomes such as risks of severe grading of age-related white matter changes and the proportion of hypertension were significantly related to these subgroups. Our study showed that automatic retinal image analysis is a convenient and non-invasive screening tool for detecting age-related white matter changes and cerebral small vessel disease with good overall performance. The localization analysis for various brain regions shows that the classification models on each of the six brain regions can be done, and it opens up potential future clinical application. Abstract : This study showed that automatic retinal imaging analysis is a convenient, cost-effective and non-invasive screening method to identify and localize cerebral white matter hyperintensities. Future research can correlate the presence of white matter hyperintensities in specific brain regions with various neurological conditions. Graphical Abstract: