Alzheimer's Disease

A Multi-Label Deep Learning Model for Detailed Classification of Alzheimer's Disease.

BACKGROUND: Accurate diagnosis and classification of Alzheimer's disease (AD) are crucial for effective treatment and management. Traditional diagnostic models, largely based on binary classification systems, fail to adequately capture the complexities and variations across different stages and subtypes of AD, limiting their clinical utility.

Creating Chemiluminescence Signature Arrays Coupled with Machine Learning for Alzheimer's Disease Serum Diagnosis.

Although omics and multi-omics approaches are the most used methods to create signature arrays for liquid biopsy, the high cost of omics technologies still largely limits their wide applications for point-of-care. Inspired by the bat echolocation mechanism, we propose an "echoes" approach for creating chemiluminescence signatures via screening of a compound library, and serum samples of Alzheimer'...

Dynamic and concordance-assisted learning for risk stratification with application to Alzheimer's disease.

Dynamic prediction models capable of retaining accuracy by evolving over time could play a significant role for monitoring disease progression in clin...

Leveraging Self-Training and Variational Autoencoder for Agitation Detection in People with Dementia Using Wearable Sensors

Dementia is a neurodegenerative disorder that has been growing among elder people over the past decades. This growth profoundly impacts the quality ...

AlzheimerRAG: Multimodal Retrieval Augmented Generation for PubMed articles

Recent advancements in generative AI have flourished the development of highly adept Large Language Models (LLMs) that integrate diverse data types ...

Linguistic Features Extracted by GPT-4 Improve Alzheimer's Disease Detection based on Spontaneous Speech

Alzheimer's Disease (AD) is a significant and growing public health concern. Investigating alterations in speech and language patterns offers a prom...

Accurate and Efficient Algorithm for Detection of Alzheimer Disability Based on Deep Learning.

BACKGROUND/AIMS: Alzheimer's Disease (AD) is a progressive neurodegenerative disorder that severely affects cognitive functions and memory. Early dete...

G-VEval: A Versatile Metric for Evaluating Image and Video Captions Using GPT-4o

Evaluation metric of visual captioning is important yet not thoroughly explored. Traditional metrics like BLEU, METEOR, CIDEr, and ROUGE often miss ...

A Mapper Algorithm with implicit intervals and its optimization

The Mapper algorithm is an essential tool for visualizing complex, high dimensional data in topology data analysis (TDA) and has been widely used in...

segcsvd: A Convolutional Neural Network-Based Tool for Quantifying White Matter Hyperintensities in Heterogeneous Patient Cohorts.

White matter hyperintensities (WMH) of presumed vascular origin are a magnetic resonance imaging (MRI)-based biomarker of cerebral small vessel diseas...

The effectiveness of care robots in alleviating physical burden and pain for caregivers: Non-randomized prospective interventional study - Preliminary study.

BACKGROUND: Caregiver burden significantly affects both patients and caregivers but is often overlooked in clinical practice. Physical and emotional s...

Detecting Cognitive Impairment and Psychological Well-being among Older Adults Using Facial, Acoustic, Linguistic, and Cardiovascular Patterns Derived from Remote Conversations

The aging society urgently requires scalable methods to monitor cognitive decline and identify social and psychological factors indicative of dement...

A comprehensive interpretable machine learning framework for Mild Cognitive Impairment and Alzheimer's disease diagnosis

An interpretable machine learning (ML) framework is introduced to enhance the diagnosis of Mild Cognitive Impairment (MCI) and Alzheimer's disease (...

Not All Errors Are Equal: Investigation of Speech Recognition Errors in Alzheimer's Disease Detection

Automatic Speech Recognition (ASR) plays an important role in speech-based automatic detection of Alzheimer's disease (AD). However, recognition err...

Leveraging Prompt Learning and Pause Encoding for Alzheimer's Disease Detection

Compared to other clinical screening techniques, speech-and-language-based automated Alzheimer's disease (AD) detection methods are characterized by...

A Self-guided Multimodal Approach to Enhancing Graph Representation Learning for Alzheimer's Diseases

Graph neural networks (GNNs) are powerful machine learning models designed to handle irregularly structured data. However, their generic design ofte...

LVS-Net: A Lightweight Vessels Segmentation Network for Retinal Image Analysis

The analysis of retinal images for the diagnosis of various diseases is one of the emerging areas of research. Recently, the research direction has ...

Early Diagnosis of Alzheimer's Diseases and Dementia from MRI Images Using an Ensemble Deep Learning

Alzheimer's Disease (AD) is a progressive neurological disorder that can result in significant cognitive impairment and dementia. Accurate and timel...

Self-Supervised Masked Mesh Learning for Unsupervised Anomaly Detection on 3D Cortical Surfaces

Unsupervised anomaly detection in brain imaging is challenging. In this paper, we propose self-supervised masked mesh learning for unsupervised anom...

A multimodal vision transformer for interpretable fusion of functional and structural neuroimaging data.

Multimodal neuroimaging is an emerging field that leverages multiple sources of information to diagnose specific brain disorders, especially when deep...