Alzheimer's Disease

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 machi...

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 intervention...

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, sp...

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-relate...

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 t...

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 Ameri...

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...

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 mor...

Editorial: Quantitative imaging methods and analysis in Alzheimer's disease assessment.

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 i...

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 deter...

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

Clustered regularly interspaced short palindromic repeat (CRISPR)-based genome editing (GED) technol...

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...

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...

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 b...

Emerging perspectives of synaptic biomarkers in ALS and FTD.

Amyotrophic Lateral Sclerosis (ALS) and Frontotemporal Dementia (FTD) are debilitating neurodegenera...

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, ...

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 bioma...

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 a...

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, crus...

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 stimu...