[HTML][HTML] T1-weighted MRI-driven brain age estimation in Alzheimer's disease and Parkinson's disease
Neuroimaging-driven brain age estimation has introduced a robust (reliable and heritable)
biomarker for detecting and monitoring neurodegenerative diseases. Here, we computed …
biomarker for detecting and monitoring neurodegenerative diseases. Here, we computed …
Studying the Effects of Sex-Related Differences on Brain Age Prediction Using Brain MR Imaging
While utilizing machine learning models, one of the most crucial aspects is how bias and
fairness affect model outcomes for diverse demographics. This becomes especially relevant …
fairness affect model outcomes for diverse demographics. This becomes especially relevant …
Confounding Factors Mitigation in Brain Age Prediction Using MRI with Deformation Fields
The aging brain is characterized by a decline in physical and mental capacities and
susceptibility to neurological disorders. Magnetic resonance imaging (MRI) has proven to be …
susceptibility to neurological disorders. Magnetic resonance imaging (MRI) has proven to be …
Human-to-monkey transfer learning identifies the frontal white matter as a key determinant for predicting monkey brain age
The application of artificial intelligence (AI) to summarize a whole-brain magnetic resonance
image (MRI) into an effective “brain age” metric can provide a holistic, individualized, and …
image (MRI) into an effective “brain age” metric can provide a holistic, individualized, and …
Towards the interpretability of deep learning models for human neuroimaging
Brain-age (BA) estimates based on deep learning are increasingly used as neuroimaging
biomarker for brain health; however, the underlying neural features have remained unclear …
biomarker for brain health; however, the underlying neural features have remained unclear …
Improving individual brain age prediction using an ensemble deep learning framework
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 …
individual brain health and may serve as a single quantitative index for clinical and domain …
Age estimates from brain magnetic resonance images of children younger than two years of age using deep learning
M Kawaguchi, H Kidokoro, R Ito, A Shiraki… - Magnetic Resonance …, 2021 - Elsevier
The accuracy of brain age estimates from magnetic resonance (MR) images has improved
with the advent of deep learning artificial intelligence (AI) models. However, most previous …
with the advent of deep learning artificial intelligence (AI) models. However, most previous …
[HTML][HTML] Towards the interpretability of deep learning models for multi-modal neuroimaging: Finding structural changes of the ageing brain
Brain-age (BA) estimates based on deep learning are increasingly used as neuroimaging
biomarker for brain health; however, the underlying neural features have remained unclear …
biomarker for brain health; however, the underlying neural features have remained unclear …
Brain‐age prediction: Systematic evaluation of site effects, and sample age range and size
Structural neuroimaging data have been used to compute an estimate of the biological age
of the brain (brain‐age) which has been associated with other biologically and behaviorally …
of the brain (brain‐age) which has been associated with other biologically and behaviorally …
Deep learning for brain age estimation: A systematic review
Abstract Over the years, Machine Learning models have been successfully employed on
neuroimaging data for accurately predicting brain age. Deviations from the healthy brain …
neuroimaging data for accurately predicting brain age. Deviations from the healthy brain …