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Cognitive Dysfunction

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A Deep Learning-Based Ensemble Method for Early Diagnosis of Alzheimer's Disease using MRI Images.

Neuroinformatics
Recently, the early diagnosis of Alzheimer's disease has gained major attention due to the growing prevalence of the disease and the resulting costs imposed on individuals and society. The main objective of this study was to propose an ensemble metho...

Cognitive decline assessment using semantic linguistic content and transformer deep learning architecture.

International journal of language & communication disorders
BACKGROUND: Dementia is a cognitive decline that leads to the progressive deterioration of an individual's ability to perform daily activities independently. As a result, a considerable amount of time and resources are spent on caretaking. Early dete...

Deep Learning-Based Ensembling Technique to Classify Alzheimer's Disease Stages Using Functional MRI.

Journal of healthcare engineering
The major issue faced by elderly people in society is the loss of memory, difficulty learning new things, and poor judgment. This is due to damage to brain tissues, which may lead to cognitive impairment and eventually Alzheimer's. Therefore, the det...

Employing Deep-Learning Approach for the Early Detection of Mild Cognitive Impairment Transitions through the Analysis of Digital Biomarkers.

Sensors (Basel, Switzerland)
Mild cognitive impairment (MCI) is the precursor to the advanced stage of Alzheimer's disease (AD), and it is important to detect the transition to the MCI condition as early as possible. Trends in daily routines/activities provide a measurement of c...

Deep learning based diagnosis of Alzheimer's disease using FDG-PET images.

Neuroscience letters
PURPOSE: The aim of this study is to develop a deep neural network to diagnosis Alzheimer's disease and categorize the stages of the disease using FDG-PET scans. Fluorodeoxyglucose positron emission tomography (FDG-PET) is a highly effective diagnost...

c-Diadem: a constrained dual-input deep learning model to identify novel biomarkers in Alzheimer's disease.

BMC medical genomics
BACKGROUND: Alzheimer's disease (AD) is an incurable, debilitating neurodegenerative disorder. Current biomarkers for AD diagnosis require expensive neuroimaging or invasive cerebrospinal fluid sampling, thus precluding early detection. Blood-based b...

Circular-SWAT for deep learning based diagnostic classification of Alzheimer's disease: application to metabolome data.

EBioMedicine
BACKGROUND: Deep learning has shown potential in various scientific domains but faces challenges when applied to complex, high-dimensional multi-omics data. Alzheimer's Disease (AD) is a neurodegenerative disorder that lacks targeted therapeutic opti...

Distinct subtypes of spatial brain metabolism patterns in Alzheimer's disease identified by deep learning-based FDG PET clusters.

European journal of nuclear medicine and molecular imaging
PURPOSE: Alzheimer's disease (AD) is a heterogeneous disease that presents a broad spectrum of clinicopathologic profiles. To date, objective subtyping of AD independent of disease progression using brain imaging has been required. Our study aimed to...

Predicting brain age gap with radiomics and automl: A Promising approach for age-Related brain degeneration biomarkers.

Journal of neuroradiology = Journal de neuroradiologie
The Brain Age Gap (BAG), which refers to the difference between chronological age and predicted neuroimaging age, is proposed as a potential biomarker for age-related brain degeneration. However, existing brain age prediction models usually rely on a...

Improving Alzheimer Diagnoses With An Interpretable Deep Learning Framework: Including Neuropsychiatric Symptoms.

Neuroscience
Alzheimer's disease (AD) is a prevalent neurodegenerative disorder characterized by the progressive cognitive decline. Among the various clinical symptoms, neuropsychiatric symptoms (NPS) commonly occur during the course of AD. Previous researches ha...