Neuroimaging-based age prediction using machine learning is proposed as a biomarker of
brain aging, relating to cognitive performance, health outcomes and progression of …

Machine learning (ML) has transformed neuroimaging research by enabling accurate
predictions and feature extraction from large datasets. In this study, we investigate the …

Introduction The brain age score has recently been introduced for robust monitoring of brain
morphological alterations throughout the lifespan, prediction of mortality risk, and early …

Abstract Machine learning algorithms trained to recognize age-related structural changes in
magnetic resonance images (MRIs) of healthy individuals can be used to predict biological …

The discrepancy between chronological age and the apparent age of the brain based on
neuroimaging data—the brain age delta—has emerged as a reliable marker of brain health …

Brain age prediction using machine‐learning techniques has recently attracted growing
attention, as it has the potential to serve as a biomarker for characterizing the typical brain …

The disparity between the chronological age of an individual and their brain-age measured
based on biological information has the potential to offer clinically relevant biomarkers of …

Measuring differences between an individual's age and biological age with biological
information from the brain have the potential to provide biomarkers of clinically relevant …

Though aging is ubiquitous, the rate at which age-associated biological changes in the brain
occur differs substantially between individuals. Building on this, the so-called brain-age …

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 …