Machine learning in mental health: a scoping review of methods and applications
BackgroundThis paper aims to synthesise the literature on machine learning (ML) and big
data applications for mental health, highlighting current research and applications in …
data applications for mental health, highlighting current research and applications in …
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 …
Multimodal deep learning models for early detection of Alzheimer's disease stage
Most current Alzheimer's disease (AD) and mild cognitive disorders (MCI) studies use single
data modality to make predictions such as AD stages. The fusion of multiple data modalities …
data modality to make predictions such as AD stages. The fusion of multiple data modalities …
Artificial intelligence in neurodegenerative diseases: A review of available tools with a focus on machine learning techniques
Neurodegenerative diseases have shown an increasing incidence in the older population in
recent years. A significant amount of research has been conducted to characterize these …
recent years. A significant amount of research has been conducted to characterize these …
Artificial intelligence in brain MRI analysis of Alzheimer's disease over the past 12 years: A systematic review
Introduction Multiple structural brain changes in Alzheimer's disease (AD) and mild cognitive
impairment (MCI) have been revealed on magnetic resonance imaging (MRI). There is a fast …
impairment (MCI) have been revealed on magnetic resonance imaging (MRI). There is a fast …
Machine learning and novel biomarkers for the diagnosis of Alzheimer's disease
Background: Alzheimer's disease (AD) is a complex and severe neurodegenerative disease
that still lacks effective methods of diagnosis. The current diagnostic methods of AD rely on …
that still lacks effective methods of diagnosis. The current diagnostic methods of AD rely on …
Structural magnetic resonance imaging for the early diagnosis of dementia due to Alzheimer's disease in people with mild cognitive impairment
G Lombardi, G Crescioli, E Cavedo… - Cochrane Database …, 2020 - cochranelibrary.com
Background Mild cognitive impairment (MCI) due to Alzheimer's disease is the symptomatic
predementia phase of Alzheimer's disease dementia, characterised by cognitive and …
predementia phase of Alzheimer's disease dementia, characterised by cognitive and …
Random forest feature selection, fusion and ensemble strategy: Combining multiple morphological MRI measures to discriminate among healhy elderly, MCI, cMCI …
Background In the era of computer-assisted diagnostic tools for various brain diseases,
Alzheimer's disease (AD) covers a large percentage of neuroimaging research, with the …
Alzheimer's disease (AD) covers a large percentage of neuroimaging research, with the …
[HTML][HTML] Cortical graph neural network for AD and MCI diagnosis and transfer learning across populations
Combining machine learning with neuroimaging data has a great potential for early
diagnosis of mild cognitive impairment (MCI) and Alzheimer's disease (AD). However, it …
diagnosis of mild cognitive impairment (MCI) and Alzheimer's disease (AD). However, it …
Using machine learning to predict dementia from neuropsychiatric symptom and neuroimaging data
S Gill, P Mouches, S Hu, D Rajashekar… - Journal of …, 2020 - content.iospress.com
Background: Machine learning (ML) is a promising technique for patient-specific prediction
of mild cognitive impairment (MCI) and dementia development. Neuropsychiatric symptoms …
of mild cognitive impairment (MCI) and dementia development. Neuropsychiatric symptoms …