AI Medical Compendium Topic:
Alzheimer Disease

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A multilayer multimodal detection and prediction model based on explainable artificial intelligence for Alzheimer's disease.

Scientific reports
Alzheimer's disease (AD) is the most common type of dementia. Its diagnosis and progression detection have been intensively studied. Nevertheless, research studies often have little effect on clinical practice mainly due to the following reasons: (1)...

Screening of Alzheimer's disease by facial complexion using artificial intelligence.

Aging
Despite the increasing incidence and high morbidity associated with dementia, a simple, non-invasive, and inexpensive method of screening for dementia is yet to be discovered. This study aimed to examine whether artificial intelligence (AI) could dis...

Brain Asymmetry Detection and Machine Learning Classification for Diagnosis of Early Dementia.

Sensors (Basel, Switzerland)
Early identification of degenerative processes in the human brain is considered essential for providing proper care and treatment. This may involve detecting structural and functional cerebral changes such as changes in the degree of asymmetry betwee...

Cognitive and MRI trajectories for prediction of Alzheimer's disease.

Scientific reports
The concept of Mild Cognitive Impairment (MCI) is used to describe the early stages of Alzheimer's disease (AD), and identification and treatment before further decline is an important clinical task. We selected longitudinal data from the ADNI databa...

Modeling autosomal dominant Alzheimer's disease with machine learning.

Alzheimer's & dementia : the journal of the Alzheimer's Association
INTRODUCTION: Machine learning models were used to discover novel disease trajectories for autosomal dominant Alzheimer's disease.

Modular machine learning for Alzheimer's disease classification from retinal vasculature.

Scientific reports
Alzheimer's disease is the leading cause of dementia. The long progression period in Alzheimer's disease provides a possibility for patients to get early treatment by having routine screenings. However, current clinical diagnostic imaging tools do no...

Improved amyloid burden quantification with nonspecific estimates using deep learning.

European journal of nuclear medicine and molecular imaging
PURPOSE: Standardized uptake value ratio (SUVr) used to quantify amyloid-β burden from amyloid-PET scans can be biased by variations in the tracer's nonspecific (NS) binding caused by the presence of cerebrovascular disease (CeVD). In this work, we p...

Computer-Aided Diagnosis of Alzheimer's Disease through Weak Supervision Deep Learning Framework with Attention Mechanism.

Sensors (Basel, Switzerland)
Alzheimer's disease (AD) is the most prevalent neurodegenerative disease causing dementia and poses significant health risks to middle-aged and elderly people. Brain magnetic resonance imaging (MRI) is the most widely used diagnostic method for AD. H...

Deep learning detection of informative features in tau PET for Alzheimer's disease classification.

BMC bioinformatics
BACKGROUND: Alzheimer's disease (AD) is the most common type of dementia, typically characterized by memory loss followed by progressive cognitive decline and functional impairment. Many clinical trials of potential therapies for AD have failed, and ...

Fused Sparse Network Learning for Longitudinal Analysis of Mild Cognitive Impairment.

IEEE transactions on cybernetics
Alzheimer's disease (AD) is a neurodegenerative disease with an irreversible and progressive process. To understand the brain functions and identify the biomarkers of AD and early stages of the disease [also known as, mild cognitive impairment (MCI)]...