Alzheimer's Disease

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 such as pauses, repetitions, and fragmented sentences. It was trained on standard speech and may have had little or no exposure to dementia-affected speech. However, correct transcription is vital for dementia speech for cost-effective diagnosis and ...

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 without continuous clinical supervision. Timely prediction of agitation can enable early intervention, reduce caregiver burden, and improve the quality of life for both patients and caregivers. This study aimed to develop and benchmark machine learning ...

ConnectomeDiffuser: Generative AI Enables Brain Network Construction from Diffusion Tensor Imaging

Brain network analysis plays a crucial role in diagnosing and monitoring neurodegenerative disorders such as Alzheimer's disease (AD). Existing appr...

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...

Redemption Score: An Evaluation Framework to Rank Image Captions While Redeeming Image Semantics and Language Pragmatics

Evaluating image captions requires cohesive assessment of both visual semantics and language pragmatics, which is often not entirely captured by mos...

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...

The Stochastic Occupation Kernel (SOCK) Method for Learning Stochastic Differential Equations

We present a novel kernel-based method for learning multivariate stochastic differential equations (SDEs). The method follows a two-step procedure: ...

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...

Identifying Key Factors Associated with Assistive Technology Availability for Dementia Care Using Machine Learning.

This study explores the factors influencing the availability of assistive technology for people with dementia through the application of machine learn...

Deep normative modelling reveals insights into early-stage Alzheimer's disease using multi-modal neuroimaging data.

BACKGROUND: Exploring the early stages of Alzheimer's disease (AD) is crucial for timely intervention to help manage symptoms and set expectations for...

Radiogenomic Bipartite Graph Representation Learning for Alzheimer's Disease Detection

Imaging and genomic data offer distinct and rich features, and their integration can unveil new insights into the complex landscape of diseases. In ...