Early prediction of Alzheimer's disease using artificial intelligence and cortical features on T1WI sequences.

ABSTRACT:

Background

Accurately predicting the progression of mild cognitive impairment (MCI) to Alzheimer's disease (AD) is a challenging task, which is crucial for helping develop personalized treatment plans to improve prognosis.

Purpose

To develop new technology for the early prediction of AD using artificial intelligence and cortical features on MRI.

Methods

A total of 162 MCI patients were included from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. By using a 3D-MPRAGE sequence, T1W images for each patient were acquired. All patients were randomly divided into a training set (n = 112) and a validation set (n = 50) at a ratio of 7:3. Morphological features of the cerebral cortex were extracted with FreeSurfer software. Network features were

SUBMITTER: Zeng R 

PROVIDER: S-EPMC11938367 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature