Deep learning-based quantification of brain atrophy using 2D T1-weighted MRI for Alzheimer's disease classification.

ABSTRACT:

Background

Determining brain atrophy is crucial for the diagnosis of neurodegenerative diseases. Despite detailed brain atrophy assessments using three-dimensional (3D) T1-weighted magnetic resonance imaging, their practical utility is limited by cost and time. This study introduces deep learning algorithms for quantifying brain atrophy using a more accessible two-dimensional (2D) T1, aiming to achieve cost-effective differentiation of dementia of the Alzheimer's type (DAT) from cognitively unimpaired (CU), while maintaining or exceeding the performance obtained with T1-3D individuals and to accurately predict AD-specific atrophy similarity and atrophic changes [W-scores and Brain Age Index (BAI)].

Methods

Involving 924 participants (478 CU and 446 DAT), our deep learning mo

SUBMITTER: Park CJ 

PROVIDER: S-EPMC11349618 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature