Dementia

Single Domain Generalization for Alzheimer's Detection from 3D MRIs with Pseudo-Morphological Augmentations and Contrastive Learning

Although Alzheimer's disease detection via MRIs has advanced significantly thanks to contemporary deep learning models, challenges such as class imbalance, protocol variations, and limited dataset diversity often hinder their generalization capacity. To address this issue, this article focuses on the single domain generalization setting, where given the data of one domain, a model is designed an...

Single Domain Generalization for Alzheimer's Detection from 3D MRIs with Pseudo-Morphological Augmentations and Contrastive Learning

Although Alzheimer's disease detection via MRIs has advanced significantly thanks to contemporary deep learning models, challenges such as class imbalance, protocol variations, and limited dataset diversity often hinder their generalization capacity. To address this issue, this article focuses on the single domain generalization setting, where given the data of one domain, a model is designed an...

Distance Transform Guided Mixup for Alzheimer's Detection

Alzheimer's detection efforts aim to develop accurate models for early disease diagnosis. Significant advances have been achieved with convolutional...

Machine learning based prediction of cognitive metrics using major biomarkers in SuperAgers.

As populations age, understanding cognitive decline and age-related diseases like dementia has become increasingly important. "SuperAgers," individual...

Deep learning-based CAD system for Alzheimer's diagnosis using deep downsized KPLS.

Alzheimer's disease (AD) is the most prevalent type of dementia. It is linked with a gradual decline in various brain functions, such as memory. Many ...

An Explainable Diagnostic Framework for Neurodegenerative Dementias via Reinforcement-Optimized LLM Reasoning

The differential diagnosis of neurodegenerative dementias is a challenging clinical task, mainly because of the overlap in symptom presentation and ...

WhisperD: Dementia Speech Recognition and Filler Word Detection with Whisper

Whisper fails to correctly transcribe dementia speech because persons with dementia (PwDs) often exhibit irregular speech patterns and disfluencies ...

Benchmarking Early Agitation Prediction in Community-Dwelling People with Dementia Using Multimodal Sensors and Machine Learning

Agitation is one of the most common responsive behaviors in people living with dementia, particularly among those residing in community settings wit...

Reverse-Speech-Finder: A Neural Network Backtracking Architecture for Generating Alzheimer's Disease Speech Samples and Improving Diagnosis Performance

This study introduces Reverse-Speech-Finder (RSF), a groundbreaking neural network backtracking architecture designed to enhance Alzheimer's Disease...

Facial expression deep learning algorithms in the detection of neurological disorders: a systematic review and meta-analysis.

BACKGROUND: Neurological disorders, ranging from common conditions like Alzheimer's disease that is a progressive neurodegenerative disorder and remai...

AI-Driven Design and Development of Nontoxic DYRK1A Inhibitors.

Dual-specificity tyrosine-phosphorylation-regulated kinase 1A (DYRK1A) is implicated in several human diseases, including DYRK1A syndrome, cancer, and...

Multi-modal Integration Analysis of Alzheimer's Disease Using Large Language Models and Knowledge Graphs

We propose a novel framework for integrating fragmented multi-modal data in Alzheimer's disease (AD) research using large language models (LLMs) and...

Diagnostic performance of actigraphy in Alzheimer's disease using a machine learning classifier - a cross-sectional memory clinic study.

BACKGROUND: Movement patterns, activity levels and circadian rhythm are altered in Alzheimer's disease (AD) and can be assessed by actigraphy using we...

MRI-based diagnostic model for Alzheimer's disease using 3D-ResNet.

Alzheimer's disease (AD), a progressive neurodegenerative disorder, is the leading cause of dementia worldwide and remains incurable once it begins. T...

XDementNET: An Explainable Attention Based Deep Convolutional Network to Detect Alzheimer Progression from MRI data

A common neurodegenerative disease, Alzheimer's disease requires a precise diagnosis and efficient treatment, particularly in light of escalating he...

Dementia Through Different Eyes: Explainable Modeling of Human and LLM Perceptions for Early Awareness

Cognitive decline often surfaces in language years before diagnosis. It is frequently non-experts, such as those closest to the patient, who first s...

Translational approach for dementia subtype classification using convolutional neural network based on EEG connectome dynamics.

Dementia spectrum disorders, characterized by progressive cognitive decline, pose a significant global health burden. Early screening and diagnosis ar...

ElderFallGuard: Real-Time IoT and Computer Vision-Based Fall Detection System for Elderly Safety

For the elderly population, falls pose a serious and increasing risk of serious injury and loss of independence. In order to overcome this difficult...

An integrated deep learning model for early and multi-class diagnosis of Alzheimer's disease from MRI scans.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder that severely affects memory, behavior, and cognitive function. Early and accurat...

A Medical Decision Support System for Automatic Treatment Plan Generation Using Machine Learning Algorithms.

Due to demographic change, health economics is increasingly focused on the quality of life in advanced age and the associated cost aspects. Dementia i...