Advantage of grading classification using volumetric artificial intelligence for periventricular hyperintensity and deep subcortical white matter hyperintensity.

ABSTRACT: We developed and validated an artificial intelligence (AI) algorithm for the automated grading of periventricular hyperintensity (PVH) and deep subcortical white matter hyperintensity (DWMH) using magnetic resonance imaging. Overall, 246 patients were evaluated, with 137 and 109 allocated to the training and testing groups, respectively. AI-predicted grading according to the Fazekas scale was compared with expert assessments using accuracy, F1-score, and mean absolute error. Inter-rater agreement was evaluated using Fleiss' kappa to assess consistency among human raters and Cohen's kappa to measure agreement between the AI and individual human raters. The AI demonstrated superior multi-class accuracy in PVH classification compared with the human expert, achieving an accuracy of 0.798 versu

SUBMITTER: Kuwabara M 

PROVIDER: S-EPMC12624063 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature