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 …

Age-associated diseases rise as life expectancy increases. The brain presents age-related
structural changes across life, with different extends between subjects and groups. During …

Brain age is an imaging-based biomarker with excellent feasibility for characterizing
individual brain health and may serve as a single quantitative index for clinical and domain …

Precise prediction on brain age is urgently needed by many biomedical areas including
mental rehabilitation prognosis as well as various medicine or treatment trials. People …

Brain age prediction has been shown to be clinically relevant, with the errors in the
prediction associated with various psychiatric and neurological conditions. While the …

Abstract Over the years, Machine Learning models have been successfully employed on
neuroimaging data for accurately predicting brain age. Deviations from the healthy brain …

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

Deep learning has shown remarkable improvements in the analysis of medical images
without the need for engineered features. In this work, we hypothesize that deep learning is …

Deep learning has huge potential for accurate disease prediction with neuroimaging data,
but the prediction performance is often limited by training-dataset size and computing …

Brain age prediction based on imaging data and machine learning (ML) methods has great
potential to provide insights into the development of cognition and mental disorders. Though …