Dementia

Explaining graph convolutional network predictions for clinicians-An explainable AI approach to Alzheimer's disease classification.

INTRODUCTION: Graph-based representations are becoming more common in the medical domain, where each node defines a patient, and the edges signify associations between patients, relating individuals with disease and symptoms in a node classification task. In this study, a Graph Convolutional Networks (GCN) model was utilized to capture differences in neurocognitive, genetic, and brain atrophy patt...

Breaking barriers: a statistical and machine learning-based hybrid system for predicting dementia.

Dementia is a condition (a collection of related signs and symptoms) that causes a continuing deterioration in cognitive function, and millions of people are impacted by dementia every year as the world population continues to rise. Conventional approaches for determining dementia rely primarily on clinical examinations, analyzing medical records, and administering cognitive and neuropsychologica...

The ménage à trois of healthcare: the actors in after-AI era under patient consent.

INTRODUCTION: Artificial intelligence has become an increasingly powerful technological instrument in recent years, revolutionizing many sectors, incl...

Ocular biomarkers of cognitive decline based on deep-learning retinal vessel segmentation.

BACKGROUND: The current literature shows a strong relationship between retinal neuronal and vascular alterations in dementia. The purpose of the study...

Emerging perspectives of synaptic biomarkers in ALS and FTD.

Amyotrophic Lateral Sclerosis (ALS) and Frontotemporal Dementia (FTD) are debilitating neurodegenerative diseases with shared pathological features li...

Intelligent digital tools for screening of brain connectivity and dementia risk estimation in people affected by mild cognitive impairment: the AI-Mind clinical study protocol.

More than 10 million Europeans show signs of mild cognitive impairment (MCI), a transitional stage between normal brain aging and dementia stage memor...

Role of Artificial Intelligence in Multinomial Decisions and Preventative Nutrition in Alzheimer's Disease.

Alzheimer's disease (AD) affects 50 million people worldwide, an increase of 35 million since 2015, and it is known for memory loss and cognitive decl...

Mild cognitive impairment prediction and cognitive score regression in the elderly using EEG topological data analysis and machine learning with awareness assessed in affective reminiscent paradigm.

INTRODUCTION: The main objective of this study is to evaluate working memory and determine EEG biomarkers that can assist in the field of health neuro...

Enabling Lipidomic Biomarker Studies for Protected Populations by Combining Noninvasive Fingerprint Sampling with MS Analysis and Machine Learning.

Triacylglycerols and wax esters are two lipid classes that have been linked to diseases, including autism, Alzheimer's disease, dementia, cardiovascul...

Modelling phenomenological differences in aetiologically distinct visual hallucinations using deep neural networks.

Visual hallucinations (VHs) are perceptions of objects or events in the absence of the sensory stimulation that would normally support such perception...

Antioxidant and anticholinesterase properties of and venoms from the Persian Gulf.

The Persian Gulf is home to a diverse range of marine life, including various species of fish, crustaceans, mollusks, and echinoderms. This study inv...

Assess Alzheimer's Disease via Plasma Extracellular Vesicle-derived mRNA.

Alzheimer's disease (AD), the most prevalent neurodegenerative disorder globally, has emerged as a significant health concern, particularly due to the...

Using Generative Artificial Intelligence to Classify Primary Progressive Aphasia from Connected Speech.

Neurodegenerative dementia syndromes, such as Primary Progressive Aphasias (PPA), have traditionally been diagnosed based in part on verbal and nonver...

Deep learning applications in vascular dementia using neuroimaging.

PURPOSE OF REVIEW: Vascular dementia (VaD) is the second common cause of dementia after Alzheimer's disease, and deep learning has emerged as a critic...

Brain age prediction using combined deep convolutional neural network and multi-layer perceptron algorithms.

The clinical applications of brain age prediction have expanded, particularly in anticipating the onset and prognosis of various neurodegenerative dis...

DeepOmicsAE: Representing Signaling Modules in Alzheimer's Disease with Deep Learning Analysis of Proteomics, Metabolomics, and Clinical Data.

Large omics datasets are becoming increasingly available for research into human health. This paper presents DeepOmicsAE, a workflow optimized for the...

A deep learning model for Alzheimer's disease diagnosis based on patient clinical records.

BACKGROUND: Dementia, with Alzheimer's disease (AD) being the most common type of this neurodegenerative disease, is an under-diagnosed health problem...

A deep learning model for the detection of various dementia and MCI pathologies based on resting-state electroencephalography data: A retrospective multicentre study.

Dementia and mild cognitive impairment (MCI) represent significant health challenges in an aging population. As the search for noninvasive, precise an...

Classifying Alzheimer's disease and normal subjects using machine learning techniques and genetic-environmental features.

BACKGROUND: Alzheimer's disease (AD) is complicated by multiple environmental and polygenetic factors. The accuracy of artificial neural networks (ANN...

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

Recently, the early diagnosis of Alzheimer's disease has gained major attention due to the growing prevalence of the disease and the resulting costs i...