Alzheimer's Disease

Algorithmic Fairness of Machine Learning Models for Alzheimer Disease Progression.

IMPORTANCE: Predictive models using machine learning techniques have potential to improve early detection and management of Alzheimer disease (AD). However, these models potentially have biases and may perpetuate or exacerbate existing disparities.

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

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 cognitive/functional status, particularly in older adults. In this study, activity data from longitudinal monitoring through in-home ambient sensors ar...

Halloysite nanotubes-loaded conductive polymer as substrate and label material for sensitive detection of amyloid-β protein by electrochemical immunosensor.

Amyloid-beta protein (Aβ) is a unique biomarker for Alzheimer's disease (AD). The sandwich-type electrochemical immunosensor, one of the key tools for...

Joint triplet loss with semi-hard constraint for data augmentation and disease prediction using gene expression data.

The accurate prediction of patients with complex diseases, such as Alzheimer's disease (AD), as well as disease stages, including early- and late-stag...

Cholesterol Levels, Hormone Replacement Therapy, and Incident Dementia among Older Adult Women.

Previous studies revealed that hormone replacement therapy (HRT) probably has a protective effect for preventing dementia in post-menopausal women. Ho...

Retina Oculomics in Neurodegenerative Disease.

Ophthalmic biomarkers have long played a critical role in diagnosing and managing ocular diseases. Oculomics has emerged as a field that utilizes ocul...

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

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...

Deep learning based on susceptibility-weighted MR sequence for detecting cerebral microbleeds and classifying cerebral small vessel disease.

BACKGROUND: Cerebral microbleeds (CMBs) serve as neuroimaging biomarkers to assess risk of intracerebral hemorrhage and diagnose cerebral small vessel...

Class-Balanced Deep Learning with Adaptive Vector Scaling Loss for Dementia Stage Detection.

Alzheimer's disease (AD) leads to irreversible cognitive decline, with Mild Cognitive Impairment (MCI) as its prodromal stage. Early detection of AD a...

Automatic selection of spoken language biomarkers for dementia detection.

This paper analyzes diverse features extracted from spoken language to select the most discriminative ones for dementia detection. We present a two-st...

Artificial intelligence for dementia prevention.

INTRODUCTION: A wide range of modifiable risk factors for dementia have been identified. Considerable debate remains about these risk factors, possibl...

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

BACKGROUND: Alzheimer's disease (AD) is an incurable, debilitating neurodegenerative disorder. Current biomarkers for AD diagnosis require expensive n...

Aggregation-induced electrochemiluminescence enhancement of Ag-MOG for amyloid β 42 sensing.

This study aimed to introduce an immunosensor for measuring amyloid β 42 (Aβ) levels by aggregation-induced enhanced electrochemiluminescence (ECL). M...

Speech and language processing with deep learning for dementia diagnosis: A systematic review.

Dementia is a progressive neurodegenerative disease that burdens the person living with the disease, their families, and medical and social services. ...

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

BACKGROUND: Deep learning has shown potential in various scientific domains but faces challenges when applied to complex, high-dimensional multi-omics...

TFEB/LAMP2 contributes to PM-induced autophagy-lysosome dysfunction and alpha-synuclein dysregulation in astrocytes.

Atmospheric particulate matter (PM) exacerbates the risk factor for Alzheimer's and Parkinson's diseases (PD) by promoting the alpha-synuclein (α-syn)...

Spectral analysis and Bi-LSTM deep network-based approach in detection of mild cognitive impairment from electroencephalography signals.

Mild cognitive impairment (MCI) is a neuropsychological syndrome that is characterized by cognitive impairments. It typically affects adults 60 years ...

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

PURPOSE: Alzheimer's disease (AD) is a heterogeneous disease that presents a broad spectrum of clinicopathologic profiles. To date, objective subtypin...

Overview of methods and available tools used in complex brain disorders.

Complex brain disorders, including Alzheimer's dementia, sleep disorders, and epilepsy, are chronic conditions that have high prevalence individually ...

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

The Brain Age Gap (BAG), which refers to the difference between chronological age and predicted neuroimaging age, is proposed as a potential biomarker...