Classification-Biased Apparent Brain Age for the Prediction of Alzheimer's Disease.

ABSTRACT: Machine Learning methods are often adopted to infer useful biomarkers for the early diagnosis of many neurodegenerative diseases and, in general, of neuroanatomical ageing. Some of these methods estimate the subject age from morphological brain data, which is then indicated as "brain age". The difference between such a predicted brain age and the actual chronological age of a subject can be used as an indication of a pathological deviation from normal brain ageing. An important use of the brain age model as biomarker is the prediction of Alzheimer's disease (AD) from structural Magnetic Resonance Imaging (MRI). Many different machine learning approaches have been applied to this specific predictive task, some of which have achieved high accuracy at the expense of the descriptiveness of the

SUBMITTER: Varzandian A 

PROVIDER: S-EPMC8193935 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature