AIMC Topic: Alzheimer Disease
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Random forest prediction of Alzheimer's disease using pairwise selection from time series data.
PloS one
Feb 14, 2019
Time-dependent data collected in studies of Alzheimer's disease usually has missing and irregularly sampled data points. For this reason time series methods which assume regular sampling cannot be applied directly to the data without a pre-processing...
New-Onset Alzheimer's Disease and Normal Subjects 100% Differentiated by P300.
American journal of Alzheimer's disease and other dementias
Feb 7, 2019
Previous work has suggested that evoked potential analysis might allow the detection of subjects with new-onset Alzheimer's disease, which would be useful clinically and personally. Here, it is described how subjects with new-onset Alzheimer's diseas...
Multi-task exclusive relationship learning for alzheimer's disease progression prediction with longitudinal data.
Medical image analysis
Jan 30, 2019
Alzheimer's disease (AD) is a neurodegenerative disorder characterized by progressive impairment of memory and other cognitive functions. Currently, many multi-task learning approaches have been proposed to predict the disease progression at the earl...
RNN-based longitudinal analysis for diagnosis of Alzheimer's disease.
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Jan 26, 2019
Alzheimer's disease (AD) is an irreversible neurodegenerative disorder with progressive impairment of memory and other mental functions. Magnetic resonance images (MRI) have been widely used as an important imaging modality of brain for AD diagnosis ...
A parameter-efficient deep learning approach to predict conversion from mild cognitive impairment to Alzheimer's disease.
NeuroImage
Jan 14, 2019
Some forms of mild cognitive impairment (MCI) are the clinical precursors of Alzheimer's disease (AD), while other MCI types tend to remain stable over-time and do not progress to AD. To identify and choose effective and personalized strategies to pr...
Training recurrent neural networks robust to incomplete data: Application to Alzheimer's disease progression modeling.
Medical image analysis
Jan 12, 2019
Disease progression modeling (DPM) using longitudinal data is a challenging machine learning task. Existing DPM algorithms neglect temporal dependencies among measurements, make parametric assumptions about biomarker trajectories, do not model multip...
Hierarchical Fully Convolutional Network for Joint Atrophy Localization and Alzheimer's Disease Diagnosis Using Structural MRI.
IEEE transactions on pattern analysis and machine intelligence
Dec 21, 2018
Structural magnetic resonance imaging (sMRI) has been widely used for computer-aided diagnosis of neurodegenerative disorders, e.g., Alzheimer's disease (AD), due to its sensitivity to morphological changes caused by brain atrophy. Recently, a few de...
Automated classification of Alzheimer's disease and mild cognitive impairment using a single MRI and deep neural networks.
NeuroImage. Clinical
Dec 18, 2018
We built and validated a deep learning algorithm predicting the individual diagnosis of Alzheimer's disease (AD) and mild cognitive impairment who will convert to AD (c-MCI) based on a single cross-sectional brain structural MRI scan. Convolutional n...
Antemortem CSF A42/A40 ratio predicts Alzheimer's disease pathology better than A42 in rapidly progressive dementias.
Annals of clinical and translational neurology
Dec 14, 2018
OBJECTIVE: Despite the critical importance of pathologically confirmed samples for biomarker validation, only a few studies have correlated CSF A42 values in vivo with postmortem Alzheimer's disease (AD) pathology, while none evaluated the CSF A42/A4...
Ultra-Low-Dose F-Florbetaben Amyloid PET Imaging Using Deep Learning with Multi-Contrast MRI Inputs.
Radiology
Dec 11, 2018
Purpose To reduce radiotracer requirements for amyloid PET/MRI without sacrificing diagnostic quality by using deep learning methods. Materials and Methods Forty data sets from 39 patients (mean age ± standard deviation [SD], 67 years ± 8), including...
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