Biomarkers for Alzheimer's disease diagnosis
V Mantzavinos, A Alexiou - Current Alzheimer Research, 2017 - ingentaconnect.com
Objective: The dramatic increase in the population with dementia expected in the next
decades is accompanied by the establishment of novel and innovated methods that will offer …
decades is accompanied by the establishment of novel and innovated methods that will offer …
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 …
Structural magnetic resonance imaging for the early diagnosis of dementia due to Alzheimer's disease in people with mild cognitive impairment
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 …
Convolutional neural networks-based MRI image analysis for the Alzheimer's disease prediction from mild cognitive impairment
Mild cognitive impairment (MCI) is the prodromal stage of Alzheimer's disease (AD).
Identifying MCI subjects who are at high risk of converting to AD is crucial for effective …
Identifying MCI subjects who are at high risk of converting to AD is crucial for effective …
Standardized evaluation of algorithms for computer-aided diagnosis of dementia based on structural MRI: the CADDementia challenge
Algorithms for computer-aided diagnosis of dementia based on structural MRI have
demonstrated high performance in the literature, but are difficult to compare as different data …
demonstrated high performance in the literature, but are difficult to compare as different data …
Detection of subjects and brain regions related to Alzheimer's disease using 3D MRI scans based on eigenbrain and machine learning
Purpose: Early diagnosis or detection of Alzheimer's disease (AD) from the normal elder
control (NC) is very important. However, the computer-aided diagnosis (CAD) was not …
control (NC) is very important. However, the computer-aided diagnosis (CAD) was not …
Lifespan changes of the human brain in Alzheimer's disease
Brain imaging studies have shown that slow and progressive cerebral atrophy characterized
the development of Alzheimer's Disease (AD). Despite a large number of studies dedicated …
the development of Alzheimer's Disease (AD). Despite a large number of studies dedicated …
[HTML][HTML] Modeling and prediction of clinical symptom trajectories in Alzheimer's disease using longitudinal data
Computational models predicting symptomatic progression at the individual level can be
highly beneficial for early intervention and treatment planning for Alzheimer's disease (AD) …
highly beneficial for early intervention and treatment planning for Alzheimer's disease (AD) …
Rey's Auditory Verbal Learning Test scores can be predicted from whole brain MRI in Alzheimer's disease
Abstract Rey's Auditory Verbal Learning Test (RAVLT) is a powerful neuropsychological tool
for testing episodic memory, which is widely used for the cognitive assessment in dementia …
for testing episodic memory, which is widely used for the cognitive assessment in dementia …
[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 …