Deep Learning Analysis of White Matter Hyperintensity and its Association with Comprehensive Vascular Factors in Two Large General Populations.

Although the relationships between basic clinical parameters and white matter hyperintensity (WMH) have been studied, the associations between vascular factors and WMH volume in general populations remain unclear. We investigated the associations between clinical parameters including comprehensive vascular factors and WMH in two large general populations. This retrospective, cross-sectional study involved two populations: individuals who underwent general health examinations at the Asan Medical Center (AMC) and participants from a regional cohort, the Korean Genome and Epidemiology Study (KoGES). WMH volume was quantified using the deep learning model nnU-Net. The associations between vascular factors and WMH volume were analyzed using multivariate linear regression. Individuals in the AMC cohort (n = 7471) had a mean [SD] age of 58.0 [9.2] years, and the KoGES participants (n = 2511), 59.2 [6.8] years. The normalized and logit-transformed WMH volumes for the AMC and KoGES were - 8.5 [1.3] and - 7.9 [1.2], respectively. The presence of carotid plaque, brachial-ankle pulse wave velocity, Agaston score, and coronary artery stenosis were associated with WMH volume after adjustments (AMC: carotid plaque β 0.13; 95% CI, 0.06-0.20; p < 0.001, baPWV β 0.001; CI 0-0.001; p < 0.001, Agaston score β 0.0003; CI 0.0001-0.0005; p < 0.001, minimal-to-mild coronary artery stenosis β 0.20; CI 0.12-0.29; p < 0.001, moderate-to-severe coronary artery stenosis β 0.30; CI 0.15-0.44; p < 0.001, KoGES: carotid plaque β 0.15; CI 0.02-0.27; p = 0.02, baPWV β 0.0004; CI 0-0.001; p = 0.001). Vascular parameters, reflecting atherosclerotic changes in carotid and coronary arteries and arterial stiffness, were independently associated with WMH volume in the general population.