Data Sheet 1_Artificial intelligence for white matter hyperintensity segmentation: performance, determinants, and clinical implications.docx

Accurate quantification is essential yet manual segmentation is time-consuming and variable. We systematically evaluated AI performance for automated WMH segmentation across disease contexts and identified factors influencing model performance.</p>Methods<p>Following PRISMA guidelines, we searched PubMed, Embase, Web of Science, Scopus, and IEEE Xplore from database inception to February 3, 2026 (PROSPERO: CRD420261355769).

Studies employing AI-based WMH segmentation and reporting Dice similarity coefficient (DSC) were included. A random-effects meta-analysis was conducted, along with subgroup analyses, meta-regression, sensitivity analyses, and publication bias assessment.</p>Results<p>26 studies comprising 4,288 participants contributed to quantitative syntheses: 13 evaluated WMH segmentation in general cohorts, and 13 evaluated AD or CSVD-specific cohorts.