Application of Fully Convolutional Neural Networks in the Assessment of Cerebral White Matter Involvement in Primary Sjogren's Syndrome.

ABSTRACT: Central nervous system (CNS) involvement in primary Sjögren's syndrome (pSS), although less frequent, can lead to serious complications. Our study aimed to assess white matter (WM) tract integrity, identify specific regions of disruption, quantify diffusion tensor imaging (DTI) metrics, and correlate these findings with rheumatologic factors. Thirty-three patients with pSS and twenty-six healthy subjects included in the control group, matched by gender and age were studied by performing brain DTI, which was reprocessed by the TractSeg algorithm based on fully convolutional neural networks (FCNN). The result was the segmentation of 72 main WM tracts, which were used to calculate quantitative values (fractional anisotropy - FA) of WM integrity. Finally, correlations of these values with rheu

SUBMITTER: Sobanski M 

PROVIDER: S-EPMC12748124 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature