Background: Neuroimage analysis has made it possible to perform various anatomical
analyses of the brain regions and helps detect different brain conditions/disorders. Recently …

Marked changes occur in the brain during people's lives, and individual rates of aging have
revealed pronounced differences, giving rise to subject-specific brainprints that are the …

Machine learning (ML) algorithms play a vital role in the brain age estimation frameworks.
The impact of regression algorithms on prediction accuracy in the brain age estimation …

The deviation between chronological age and age predicted using brain MRI is a putative
marker of overall brain health. Age prediction based on structural MRI data shows high …

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 …

Introduction retinal age derived from fundus images using deep learning has been verified
as a novel biomarker of ageing. We aim to investigate the association between retinal age …

The imaging-based method of brainAGE aims to characterize an individual's vulnerability to
age-related brain changes. The present study systematically reviewed brainAGE findings in …

Understanding age acceleration, the discordance between biological and chronological
age, in the brain can reveal mechanistic insights into normal physiology as well as elucidate …

Abstract Machine learning can reliably predict individual age from MRI data, revealing that
patients with neurodegenerative disorders show an elevated biological age. A surprising …

Neuroimaging‐driven brain age estimation has become popular in measuring brain aging
and identifying neurodegenerations. However, the single estimated brain age (gap) …