The strategic use of artificial intelligence in the digital era: Systematic literature review and future research directions
AFS Borges, FJB Laurindo, MM Spínola… - International journal of …, 2021 - Elsevier
Artificial Intelligence tools have attracted attention from the literature and business
organizations in the last decade, especially by the advances in machine learning …
organizations in the last decade, especially by the advances in machine learning …
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 …
Applications of deep learning techniques for automated multiple sclerosis detection using magnetic resonance imaging: A review
Multiple Sclerosis (MS) is a type of brain disease which causes visual, sensory, and motor
problems for people with a detrimental effect on the functioning of the nervous system. In …
problems for people with a detrimental effect on the functioning of the nervous system. In …
Diagnosis of brain diseases in fusion of neuroimaging modalities using deep learning: A review
Brain diseases, including tumors and mental and neurological disorders, seriously threaten
the health and well-being of millions of people worldwide. Structural and functional …
the health and well-being of millions of people worldwide. Structural and functional …
Machine learning techniques for diagnosis of alzheimer disease, mild cognitive disorder, and other types of dementia
Alzheimer's disease (AD) is one of the most common form of dementia which mostly affects
elderly people. AD identification in early stages is a difficult task in medical practice and …
elderly people. AD identification in early stages is a difficult task in medical practice and …
Hi-GCN: A hierarchical graph convolution network for graph embedding learning of brain network and brain disorders prediction
Purpose Recently, brain connectivity networks have been used for the classification of
neurological disorder, such as Autism Spectrum Disorders (ASD) or Alzheimer's disease …
neurological disorder, such as Autism Spectrum Disorders (ASD) or Alzheimer's disease …
Deep learning for Alzheimer's disease diagnosis: A survey
M Khojaste-Sarakhsi, SS Haghighi… - Artificial intelligence in …, 2022 - Elsevier
Alzheimer's Disease (AD) is an irreversible neurodegenerative disease that results in a
progressive decline in cognitive abilities. Since AD starts several years before the onset of …
progressive decline in cognitive abilities. Since AD starts several years before the onset of …
[HTML][HTML] An MRI-based deep learning approach for accurate detection of Alzheimer's disease
Alzheimer's disease (AD) is the most prevalent type of dementia of the nervous system that
causes many brain functions to weaken (eg, memory loss). Non-invasive early diagnosis of …
causes many brain functions to weaken (eg, memory loss). Non-invasive early diagnosis of …
[HTML][HTML] Medical deep learning—A systematic meta-review
Deep learning has remarkably impacted several different scientific disciplines over the last
few years. For example, in image processing and analysis, deep learning algorithms were …
few years. For example, in image processing and analysis, deep learning algorithms were …
Determination of an optimal control strategy for vaccine administration in COVID-19 pandemic treatment
Background and objective For decades, mathematical models have been used to predict the
behavior of physical and biological systems, as well as to define strategies aiming at the …
behavior of physical and biological systems, as well as to define strategies aiming at the …