AI Medical Compendium Topic:
Alzheimer Disease

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Use of deep learning genomics to discriminate Alzheimer's disease and healthy controls.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Alzheimer's disease (AD) is the most prevalent neurodegenerative disorder and the most common form of dementia in the elderly. Because gene is an important clinical risk factor resulting in AD, genomic studies, such as genome-wide association studies...

Nonlinear registration as an effective preprocessing technique for Deep learning based classification of disease.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
A number of machine learning (ML), and particularly in recent years, deep learning (DL) approaches have been proposed for automatic classification of Alzheimer's disease (AD) using brain structural magnetic resonance imaging (MRI) data. However, the ...

Input Agnostic Deep Learning for Alzheimer's Disease Classification Using Multimodal MRI Images.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Alzheimer's disease (AD) is a progressive brain disorder that causes memory and functional impairments. The advances in machine learning and publicly available medical datasets initiated multiple studies in AD diagnosis. In this work, we utilize a mu...

Data-Limited Deep Learning Methods for Mild Cognitive Impairment Classification in Alzheimer's Disease Patients.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Mild Cognitive Impairment (MCI) is the stage between the declining of normal brain function and the more serious decline of dementia. Alzheimer's disease (AD) is one of the leading forms of dementia. Although MCI does not always lead to AD, an early ...

Federated Learning via Conditional Mutual Learning for Alzheimer's Disease Classification on T1w MRI.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Data-driven deep learning has been considered a promising method for building powerful models for medical data, which often requires a large amount of diverse data to be sufficiently effective. However, the expensive cost of collecting and the privac...

Deep Learning on SDF for Classifying Brain Biomarkers.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Biomarkers are one of the primary medical signs to facilitate the early detection of Alzheimer's disease. The small beta-amyloid (Aβ) peptide is an important indicator for the disease. However, current methods to detect Aβ pathology are either invasi...

[Retinal Imaging as Potential Biomarkers for Dementia].

Brain and nerve = Shinkei kenkyu no shinpo
Alzheimer's disease (AD) is a leading cause of dementia, and the current diagnostic methods of AD, such as positron emission tomography imaging, have a high cost and poor accessibility. Amyloidβ accumulates in the brain long before the symptomatic on...

[A Preliminary Study of Applying Geometric Deep Learning in Brain Morphometry for Diagnosis of Alzheimer's Disease].

Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition
OBJECTIVE: A predictive model of Alzheimer's disease (AD) was established based on brain surface meshes and geometric deep learning, and its performance was evaluated.

Converting disease maps into heavyweight ontologies: general methodology and application to Alzheimer's disease.

Database : the journal of biological databases and curation
Omics technologies offer great promises for improving our understanding of diseases. The integration and interpretation of such data pose major challenges, calling for adequate knowledge models. Disease maps provide curated knowledge about disorders'...