ArticleJournal of imaging informatics in medicine2025
Deep Learning Analysis of White Matter Hyperintensity and its Association with Comprehensive Vascular Factors in Two Large General Populations.
Article in Journal of imaging informatics in medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
Abstract
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.