Automated Deep Learning Pipeline for Callosal Angle Quantification.

ABSTRACT:

Background and purpose

Normal pressure hydrocephalus (NPH) is a potentially treatable neurodegenerative disorder that remains underdiagnosed due to its clinical overlap with other conditions and the labor-intensive nature of manual imaging analyses. Imaging biomarkers, such as the callosal angle (CA), Evans Index (EI), and Disproportionately Enlarged Subarachnoid Space Hydrocephalus (DESH), play a crucial role in NPH diagnosis but are often limited by subjective interpretations. To address these challenges, we developed a fully automated and robust deep learning framework for measuring the CA directly from raw T1 MPRAGE and non-MPRAGE MRI scans.

Materials and methods

Our method integrates two complementary modules. First, a BrainSignsNET model is employed to accurately detec

SUBMITTER: Barough SS 

PROVIDER: S-EPMC12393603 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature