Brain age prediction using combined deep convolutional neural network and multi-layer perceptron algorithms.

ABSTRACT: The clinical applications of brain age prediction have expanded, particularly in anticipating the onset and prognosis of various neurodegenerative diseases. In the current study, we proposed a deep learning algorithm that leverages brain structural imaging data and enhances prediction accuracy by integrating biological sex information. Our model for brain age prediction, built on deep neural networks, employed a dataset of 3004 healthy subjects aged 18 and above. The T1-weighted images were minimally preprocessed and analyzed using the convolutional neural network (CNN) algorithm. The categorical sex information was then incorporated using the multi-layer perceptron (MLP) algorithm. We trained and validated both a CNN-only algorithm (utilizing only brain structural imaging data), and a com

SUBMITTER: Joo Y 

PROVIDER: S-EPMC10725434 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature