Early diagnosis of Alzheimer's disease based on deep learning: A systematic review
Background The improvement of health indicators and life expectancy, especially in
developed countries, has led to population growth and increased age-related diseases …
developed countries, has led to population growth and increased age-related diseases …
Artificial intelligence for brain diseases: A systematic review
Artificial intelligence (AI) is a major branch of computer science that is fruitfully used for
analyzing complex medical data and extracting meaningful relationships in datasets, for …
analyzing complex medical data and extracting meaningful relationships in datasets, for …
A mutual multi-scale triplet graph convolutional network for classification of brain disorders using functional or structural connectivity
Brain connectivity alterations associated with mental disorders have been widely reported in
both functional MRI (fMRI) and diffusion MRI (dMRI). However, extracting useful information …
both functional MRI (fMRI) and diffusion MRI (dMRI). However, extracting useful information …
Functional magnetic resonance imaging, deep learning, and Alzheimer's disease: A systematic review
SL Warren, AA Moustafa - Journal of Neuroimaging, 2023 - Wiley Online Library
Alzheimer's disease (AD) is currently diagnosed using a mixture of psychological tests and
clinical observations. However, these diagnoses are not perfect, and additional diagnostic …
clinical observations. However, these diagnoses are not perfect, and additional diagnostic …
A survey on deep learning for neuroimaging-based brain disorder analysis
Deep learning has recently been used for the analysis of neuroimages, such as structural
magnetic resonance imaging (MRI), functional MRI, and positron emission tomography …
magnetic resonance imaging (MRI), functional MRI, and positron emission tomography …
Multicenter and multichannel pooling GCN for early AD diagnosis based on dual-modality fused brain network
For significant memory concern (SMC) and mild cognitive impairment (MCI), their
classification performance is limited by confounding features, diverse imaging protocols, and …
classification performance is limited by confounding features, diverse imaging protocols, and …
Unsupervised cross-domain functional MRI adaptation for automated major depressive disorder identification
Resting-state functional magnetic resonance imaging (rs-fMRI) data have been widely used
for automated diagnosis of brain disorders such as major depressive disorder (MDD) to …
for automated diagnosis of brain disorders such as major depressive disorder (MDD) to …
Alzheimer's disease diagnosis from single and multimodal data using machine and deep learning models: Achievements and future directions
Alzheimer's Disease (AD) is the most prevalent and rapidly progressing neurodegenerative
disorder among the elderly and is a leading cause of dementia. AD results in significant …
disorder among the elderly and is a leading cause of dementia. AD results in significant …
Illuminating the black box: interpreting deep neural network models for psychiatric research
Y Sheu - Frontiers in Psychiatry, 2020 - frontiersin.org
Psychiatric research is often confronted with complex abstractions and dynamics that are not
readily accessible or well-defined to our perception and measurements, making data-driven …
readily accessible or well-defined to our perception and measurements, making data-driven …
Multi-site clustering and nested feature extraction for identifying autism spectrum disorder with resting-state fMRI
Brain functional connectivity (FC) derived from resting-state functional magnetic resonance
imaging (rs-fMRI) has been widely employed to study neuropsychiatric disorders such as …
imaging (rs-fMRI) has been widely employed to study neuropsychiatric disorders such as …