Deep neural networks have shown promise in predicting the chronological age of a healthy
brain using T1-weighted magnetic resonance images (T1 MRIs). This predicted brain age …

Chronological age of healthy brain is able to be predicted using deep neural networks from
T1-weighted magnetic resonance images (T1 MRIs), and the predicted brain age could …

Chronological age of healthy people is able to be predicted accurately using deep neural
networks from neuroimaging data, and the predicted brain age could serve as a biomarker …

Deep neural networks (DNN) have been designed to predict the chronological age of a
healthy brain from T1-weighted magnetic resonance images (T1 MRIs), and the predicted …

As age increases, human brains will be aged, and people tend to experience cognitive
decline with a higher risk of neuro-degenerative disease and dementia. Recently, it was …

Currently, significant progress has been made in predicting brain age from structural
Magnetic Resonance Imaging (sMRI) data using deep learning techniques. However …

In the process of human aging, significant age-related changes occur in brain tissue. To
assist individuals in assessing the degree of brain aging, screening for disease risks, and …

Accelerated brain aging and abnormalities are associated with variations in brain patterns.
Effective and reliable assessment methods are required to accurately classify and estimate …

Convolutional neural networks (CNNs) have been used widely to predict biological brain
age based on brain magnetic resonance (MR) images. However, CNNs focus mainly on …

Predicted brain age could be used to measure individual brain status over development and
degeneration, which could also indicate the potential risk of age-related brain disorders …