Alzheimer's Disease

Developing an ontology of non-pharmacological treatment for emotional and mood disturbances in dementia.

Emotional and mood disturbances are common in people with dementia. Non-pharmacological interventions are beneficial for managing these disturbances. However, effectively applying these interventions, particularly in the person-centred approach, is a complex and knowledge-intensive task. Healthcare professionals need the assistance of tools to obtain all relevant information that is often buried i...

Enhancing foveal avascular zone analysis for Alzheimer's diagnosis with AI segmentation and machine learning using multiple radiomic features.

We propose a hybrid technique that employs artificial intelligence (AI)-based segmentation and machine learning classification using multiple features extracted from the foveal avascular zone (FAZ)-a retinal biomarker for Alzheimer's disease-to improve the disease diagnostic performance. Imaging data of optical coherence tomography angiography from 37 patients with Alzheimer's disease and 48 healt...

Comparing the performance of statistical, machine learning, and deep learning algorithms to predict time-to-event: A simulation study for conversion to mild cognitive impairment.

Mild Cognitive Impairment (MCI) is a condition characterized by a decline in cognitive abilities, specifically in memory, language, and attention, tha...

NeuroAIreh@b: an artificial intelligence-based methodology for personalized and adaptive neurorehabilitation.

Cognitive impairments are a prevalent consequence of acquired brain injury, dementia, and age-related cognitive decline, hampering individuals' daily ...

Harnessing Transfer Learning for Dementia Prediction: Leveraging Sex-Different Mild Cognitive Impairment Prognosis.

This paper presents a machine learning-based prediction for dementia, leveraging transfer learning to reuse the knowledge learned from prediction of m...

Estimating dementia risk in an African American population using the DCTclock.

The prevalence of Alzheimer's disease (AD) and related dementias (ADRD) is increasing. African Americans are twice as likely to develop dementia than ...

Deep insights into MCI diagnosis: A comparative deep learning analysis of EEG time series.

BACKGROUND: Individuals in the early stages of Alzheimer's Disease (AD) are typically diagnosed with Mild Cognitive Impairment (MCI). MCI represents a...

Determination of Alzheimer's disease based on morphology and atrophy using machine learning combined with automated segmentation.

BACKGROUND: To evaluate the degree of cerebral atrophy for Alzheimer's disease (AD), voxel-based morphometry has been performed with magnetic resonanc...

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 ass...

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 pe...

Advancing genome editing with artificial intelligence: opportunities, challenges, and future directions.

Clustered regularly interspaced short palindromic repeat (CRISPR)-based genome editing (GED) technologies have unlocked exciting possibilities for und...

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...