[HTML][HTML] Accurate brain‐age models for routine clinical MRI examinations
DA Wood, S Kafiabadi, A Al Busaidi, E Guilhem… - Neuroimage, 2022 - Elsevier
Convolutional neural networks (CNN) can accurately predict chronological age in healthy
individuals from structural MRI brain scans. Potentially, these models could be applied …
individuals from structural MRI brain scans. Potentially, these models could be applied …
Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker
Abstract Machine learning analysis of neuroimaging data can accurately predict
chronological age in healthy people. Deviations from healthy brain ageing have been …
chronological age in healthy people. Deviations from healthy brain ageing have been …
[HTML][HTML] Learning patterns of the ageing brain in MRI using deep convolutional networks
Both normal ageing and neurodegenerative diseases cause morphological changes to the
brain. Age-related brain changes are subtle, nonlinear, and spatially and temporally …
brain. Age-related brain changes are subtle, nonlinear, and spatially and temporally …
Estimating brain age based on a uniform healthy population with deep learning and structural magnetic resonance imaging
Numerous studies have established that estimated brain age constitutes a valuable
biomarker that is predictive of cognitive decline and various neurological diseases. In this …
biomarker that is predictive of cognitive decline and various neurological diseases. In this …
MRI signatures of brain age and disease over the lifespan based on a deep brain network and 14 468 individuals worldwide
Deep learning has emerged as a powerful approach to constructing imaging signatures of
normal brain ageing as well as of various neuropathological processes associated with …
normal brain ageing as well as of various neuropathological processes associated with …
Benchmarking the generalizability of brain age models: Challenges posed by scanner variance and prediction bias
Abstract Machine learning has been increasingly applied to neuroimaging data to predict
age, deriving a personalized biomarker with potential clinical applications. The scientific and …
age, deriving a personalized biomarker with potential clinical applications. The scientific and …
Anatomically interpretable deep learning of brain age captures domain-specific cognitive impairment
The gap between chronological age (CA) and biological brain age, as estimated from
magnetic resonance images (MRIs), reflects how individual patterns of neuroanatomic aging …
magnetic resonance images (MRIs), reflects how individual patterns of neuroanatomic aging …
[HTML][HTML] Deep neural networks learn general and clinically relevant representations of the ageing brain
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 …
neuroimaging data—the brain age delta—has emerged as a reliable marker of brain health …
Machine learning for brain age prediction: Introduction to methods and clinical applications
The rise of machine learning has unlocked new ways of analysing structural neuroimaging
data, including brain age prediction. In this state-of-the-art review, we provide an …
data, including brain age prediction. In this state-of-the-art review, we provide an …
Prediction of brain age using structural magnetic resonance imaging: A comparison of accuracy and test–retest reliability of publicly available software packages
Brain age prediction algorithms using structural magnetic resonance imaging (MRI) aim to
assess the biological age of the human brain. The difference between a person's …
assess the biological age of the human brain. The difference between a person's …