Machine learning methods for predicting progression from mild cognitive impairment to Alzheimer's disease dementia: a systematic review
S Grueso, R Viejo-Sobera - Alzheimer's research & therapy, 2021 - Springer
Background An increase in lifespan in our society is a double-edged sword that entails a
growing number of patients with neurocognitive disorders, Alzheimer's disease being the …
growing number of patients with neurocognitive disorders, Alzheimer's disease being the …
A review on neuroimaging-based classification studies and associated feature extraction methods for Alzheimer's disease and its prodromal stages
Neuroimaging has made it possible to measure pathological brain changes associated with
Alzheimer's disease (AD) in vivo. Over the past decade, these measures have been …
Alzheimer's disease (AD) in vivo. Over the past decade, these measures have been …
A multilayer multimodal detection and prediction model based on explainable artificial intelligence for Alzheimer's disease
Alzheimer's disease (AD) is the most common type of dementia. Its diagnosis and
progression detection have been intensively studied. Nevertheless, research studies often …
progression detection have been intensively studied. Nevertheless, research studies often …
Brain MRI analysis for Alzheimer's disease diagnosis using an ensemble system of deep convolutional neural networks
Alzheimer's disease is an incurable, progressive neurological brain disorder. Earlier
detection of Alzheimer's disease can help with proper treatment and prevent brain tissue …
detection of Alzheimer's disease can help with proper treatment and prevent brain tissue …
Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls
Neuroimaging-based single subject prediction of brain disorders has gained increasing
attention in recent years. Using a variety of neuroimaging modalities such as structural …
attention in recent years. Using a variety of neuroimaging modalities such as structural …
Multimodal neuroimaging feature learning with multimodal stacked deep polynomial networks for diagnosis of Alzheimer's disease
The accurate diagnosis of Alzheimer's disease (AD) and its early stage, ie, mild cognitive
impairment, is essential for timely treatment and possible delay of AD. Fusion of multimodal …
impairment, is essential for timely treatment and possible delay of AD. Fusion of multimodal …
Hierarchical feature representation and multimodal fusion with deep learning for AD/MCI diagnosis
For the last decade, it has been shown that neuroimaging can be a potential tool for the
diagnosis of Alzheimer's Disease (AD) and its prodromal stage, Mild Cognitive Impairment …
diagnosis of Alzheimer's Disease (AD) and its prodromal stage, Mild Cognitive Impairment …
Machine learning framework for early MRI-based Alzheimer's conversion prediction in MCI subjects
Mild cognitive impairment (MCI) is a transitional stage between age-related cognitive
decline and Alzheimer's disease (AD). For the effective treatment of AD, it would be …
decline and Alzheimer's disease (AD). For the effective treatment of AD, it would be …
Ensembles of deep learning architectures for the early diagnosis of the Alzheimer's disease
Computer Aided Diagnosis (CAD) constitutes an important tool for the early diagnosis of
Alzheimer's Disease (AD), which, in turn, allows the application of treatments that can be …
Alzheimer's Disease (AD), which, in turn, allows the application of treatments that can be …
Deep ensemble learning of sparse regression models for brain disease diagnosis
Recent studies on brain imaging analysis witnessed the core roles of machine learning
techniques in computer-assisted intervention for brain disease diagnosis. Of various …
techniques in computer-assisted intervention for brain disease diagnosis. Of various …