Predicting the brain age of children with cerebral palsy using a two-dimensional convolutional neural networks prediction model without gray and white matter segmentation

ABSTRACT:

Background

Abnormal brain development is common in children with cerebral palsy (CP), but there are no recent reports on the actual brain age of children with CP.

Objective

Our objective is to use the brain age prediction model to explore the law of brain development in children with CP.

Methods

A two-dimensional convolutional neural networks brain age prediction model was designed without segmenting the white and gray matter. Training and testing brain age prediction model using magnetic resonance images of healthy people in a public database. The brain age of children with CP aged 5–27 years old was predicted.

Results

The training dataset mean absolute error (MAE) = 1.85, r = 0.99; test dataset MAE = 3.98, r = 0.95. The brain age gap estimation (BrainA

SUBMITTER:  

PROVIDER: S-EPMC9730825 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature