Prediction of cognitive conversion within the Alzheimer's disease continuum using deep learning.

ABSTRACT:

Background

Early diagnosis and accurate prognosis of cognitive decline in Alzheimer's disease (AD) is important to timely assignment to optimal treatment modes. We aimed to develop a deep learning model to predict cognitive conversion to guide re-assignment decisions to more intensive therapies where needed.

Methods

Longitudinal data including five variable sets, i.e. demographics, medical history, neuropsychological outcomes, laboratory and neuroimaging results, from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort were analyzed. We first developed a deep learning model to predicted cognitive conversion using all five variable sets. We then gradually removed variable sets to obtained parsimonious models for four different years of forecasting after baseline wit

SUBMITTER: Yang S 

PROVIDER: S-EPMC11823042 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature