Multi-modal machine learning approach for early detection of neurodegenerative diseases leveraging brain MRI and wearable sensor data.

ABSTRACT: Neurodegenerative diseases, such as Alzheimer's and Parkinson's Disease, pose a significant healthcare burden to the aging population. Structural MRI brain parameters and accelerometry data from wearable devices have been proven to be useful predictors for these diseases but have been separately examined in the prior literature. This study aims to determine whether a combination of accelerometry data and MRI brain parameters may improve the detection and prognostication of Alzheimer's and Parkinson's disease, compared with MRI brain parameters alone. A cohort of 19,793 participants free of neurodegenerative disease at the time of imaging and accelerometry data capture from the UK Biobank with longitudinal follow-up was derived to test this hypothesis. Relevant structural MRI brain paramete

SUBMITTER: Li A 

PROVIDER: S-EPMC12027105 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature