AI Medical Compendium Topic:
Alzheimer Disease

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Voxel-Based Morphometry: Improving the Diagnosis of Alzheimer's Disease Based on an Extreme Learning Machine Method from the ADNI cohort.

Neuroscience
Computer-aided diagnosis has become a widely-used auxiliary tool for the diagnosis of Alzheimer's disease (AD). In this study, we developed an extreme learning machine (ELM) model to discriminate between patients with AD and normal controls (NCs) usi...

Design of Natural-Product-Inspired Multitarget Ligands by Machine Learning.

ChemMedChem
A virtual screening protocol based on machine learning models was used to identify mimetics of the natural product (-)-galantamine. This fully automated approach identified eight compounds with bioactivities on at least one of the macromolecular targ...

Interpretable classification of Alzheimer's disease pathologies with a convolutional neural network pipeline.

Nature communications
Neuropathologists assess vast brain areas to identify diverse and subtly-differentiated morphologies. Standard semi-quantitative scoring approaches, however, are coarse-grained and lack precise neuroanatomic localization. We report a proof-of-concept...

Prediction of Alzheimer's disease dementia with MRI beyond the short-term: Implications for the design of predictive models.

NeuroImage. Clinical
Magnetic resonance imaging (MRI) volumetric measures have become a standard tool for the detection of incipient Alzheimer's Disease (AD) dementia in mild cognitive impairment (MCI). Focused on providing an earlier and more accurate diagnosis, sophist...

Systems Pharmacological Approach to Investigate the Mechanism of for Application to Alzheimer's Disease.

Molecules (Basel, Switzerland)
(OC)-a traditional Chinese medicine (TCM)-has been reported to have large numbers of flavonoids, alkaloids, and triterpenoids. The previous studies on OC for treating Alzheimer's disease (AD) only focused on single targets and its mechanisms, while ...

Deep learning only by normal brain PET identify unheralded brain anomalies.

EBioMedicine
BACKGROUND: Recent deep learning models have shown remarkable accuracy for the diagnostic classification. However, they have limitations in clinical application due to the gap between the training cohorts and real-world data. We aimed to develop a mo...

Application of Artificial Neural Networks to Identify Alzheimer's Disease Using Cerebral Perfusion SPECT Data.

International journal of environmental research and public health
The aim of this study was to demonstrate the usefulness of artificial neural networks in Alzheimer disease diagnosis (AD) using data of brain single photon emission computed tomography (SPECT). The results were compared with discriminant analysis. Th...

Machine learning based hierarchical classification of frontotemporal dementia and Alzheimer's disease.

NeuroImage. Clinical
BACKGROUND: In a clinical setting, an individual subject classification model rather than a group analysis would be more informative. Specifically, the subtlety of cortical atrophy in some frontotemporal dementia (FTD) patients and overlapping patter...

Deep Learning and Random Forest Approach for Finding the Optimal Traditional Chinese Medicine Formula for Treatment of Alzheimer's Disease.

Journal of chemical information and modeling
It has demonstrated that glycogen synthase kinase 3β (GSK3β) is related to Alzheimer's disease (AD). On the basis of the world largest traditional Chinese medicine (TCM) database, a network-pharmacology-based approach was utilized to investigate TCM ...