Comparative Analysis of Artificial Intelligence–Based Quantification versus Visual Rating of Enlarged Perivascular Spaces in the Multi-Ethnic Study of Atherosclerosis Cohort
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Le résumé fourni par la source
BACKGROUND AND PURPOSE: Enlarged perivascular spaces (PVS) are fluid-filled spaces surrounding small cerebral vessels. Current PVS research has been limited by the absence of efficient and scalable quantification tools. We compared visual ratings versus artificial intelligence (AI)-based quantification in identifying associations of PVS with vascular risk factors and cognitive performance. MATERIALS AND METHODS: This cross-sectional study included 235 participants from the Multi-Ethnic Study of Atherosclerosis who had undergone brain MRI and had available both visual ratings and AI-derived PVS quantification. Visual ratings were performed by an expert neuroradiologist (K.D.H.) using a semiquantitative scale. AI-derived counts were based on a fully automated deep learning algorithm. Visual and AI-derived counts were grouped into 4 a priori-defined anatomic locations: basal ganglia, frontoparietal cerebrum, midbrain, and cerebellum. The relationships of PVS counts with demographic characteristics, vascular risk factors, and global and domain-specific cognitive scores were examined using ordinal logistic regression (for ordinal categoric outcomes) and linear regression (for continuous outcomes) models. RESULTS: Mean age (SD) was 72.1 (6.8) years; 95 (40%) participants were men; and 126 (54%) self-reported as black. Means (SD) of AI-derived regional PVS counts were 63.7 (24.6) for basal ganglia, 414.9 (167.5) for frontoparietal cerebrum, and 9.8 (4.4) for midbrain. On visual ratings, the most prevalent count category for each region was 11-20 for basal ganglia (40%), 21-40 (31%) for frontoparietal cerebrum, and 1-5 (83%) for midbrain. For basal ganglia PVS, while both methods were associated with older age and white race/ethnicity, AI-derived counts exhibited additional associations with higher systolic blood pressure (β, 0.20; 95% CI, 0.05-0.36) and diabetes (unstandardized β coefficient [β], 11.51; 95% CI, 3.48-19.55), as well as poorer global cognition (β, -0.012; 95% CI, -0.023 to -0.0004), delayed memory (β, -0.005; 95% CI, -0.010 to -0.0005), and attention/processing speed (β, -0.005; 95% CI, -0.009 to -0.001) cognitive performance. CONCLUSIONS: In this cross-sectional study, AI-derived PVS quantification was more sensitive in detecting associations with vascular risk factors and cognitive outcomes than traditional visual ratings. AI-based quantification may aid in the analysis of large-scale epidemiologic data, advancing PVS research.
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Comparative Analysis of Artificial Intelligence–Based Quantification versus Visual Rating of Enlarged Perivascular Spaces in the Multi-Ethnic Study of Atherosclerosis Cohort
- Date Crossref
- 15/12/2025
- Éditeur
- American Society of Neuroradiology (ASNR)
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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Boston University Department of Neurology (J.R.R.) pays non établi dans la noticeUniversité ou école supérieure
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The University of Texas at San Antonio Health Science Center pays non établi dans la noticeUniversité ou école supérieure
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University of Washington the Department of Epidemiology and Cardiovascular Health Research Unit (S.R.H.) pays non établi dans la noticeUniversité ou école supérieure
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Alzheimer’s Disease Neuroimaging Initiative pays non établi dans la noticeOrganisation à but non lucratif
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Institute for Neurodegenerative Disorders pays non établi dans la noticeOrganisation à but non lucratif
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Wake Forest University pays non établi dans la noticeUniversité ou école supérieure
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University of Pennsylvania pays non établi dans la noticeUniversité ou école supérieure
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Vanderbilt University Medical Center the Vanderbilt Memory and Alzheimer's Center (T.H.) pays non établi dans la noticeÉtablissement de santé
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University of Texas Health Science Center at San Antonio From the Neuroimage Analytics Laboratory (NAL) pays non établi dans la noticeUniversité ou école supérieure
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School of Medicine the Department of Neurology (R.R.) pays non établi dans la noticeUniversité ou école supérieure
Department of Neurology (J.R.R.) — Boston University, The University of Texas at San Antonio Health Science Center et the Department of Epidemiology and Cardiovascular Health Research Unit (S.R.H.) — University of Washington, avec 7 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.