Alzheimer's Disease

Cultural variation in trust and acceptability of artificial intelligence diagnostics for dementia.

Digital health innovations hold diagnostic and therapeutic promise but may be subject to biases for underrepresented groups. We explored perceptions of using artificial intelligence (AI) diagnostics for dementia through a focus group as part of the Automated Brain Image Analysis for Timely and Equitable Dementia Diagnosis (ABATED) study. Qualitative feedback from a diverse public engagement group ...

Graph and Multi-Level Sequence Fusion Learning for Predicting the Molecular Activity of BACE-1 Inhibitors.

The development of BACE-1 (β-site amyloid precursor protein cleaving enzyme 1) inhibitors is a crucial focus in exploring early treatments for Alzheimer's disease (AD). Recently, graph neural networks (GNNs) have demonstrated significant advantages in predicting molecular activity. However, their reliance on graph structures alone often neglects explicit sequence-level semantic information. To add...

Reducing inference cost of Alzheimer's disease identification using an uncertainty-aware ensemble of uni-modal and multi-modal learners.

While multi-modal deep learning approaches trained using magnetic resonance imaging (MRI) and fluorodeoxyglucose positron emission tomography (FDG PET...

Exploring Potential Medications for Alzheimer's Disease with Psychosis by Integrating Drug Target Information into Deep Learning Models: A Data-Driven Approach.

Approximately 50% of Alzheimer's disease (AD) patients develop psychotic symptoms, leading to a subtype known as psychosis in AD (AD + P), which is as...

Hybrid-RViT: Hybridizing ResNet-50 and Vision Transformer for Enhanced Alzheimer's disease detection.

Alzheimer's disease (AD) is a leading cause of disability worldwide. Early detection is critical for preventing progression and formulating effective ...

Taylor-dingo optimized RP-net for segmentation toward Alzheimer's disease detection and classification using deep learning.

Alzheimer's Disease (AD) is a significant cause of mortality in elderly people. The diagnosing and classification of AD using conventional manual oper...

A quantitatively interpretable model for Alzheimer's disease prediction using deep counterfactuals.

Deep learning (DL) for predicting Alzheimer's disease (AD) has provided timely intervention in disease progression yet still demands attentive interpr...

Prediction of cognitive conversion within the Alzheimer's disease continuum using deep learning.

BACKGROUND: Early diagnosis and accurate prognosis of cognitive decline in Alzheimer's disease (AD) is important to timely assignment to optimal treat...

ZF-QDCNN: ZFNet and quantum dilated convolutional neural network based Alzheimer's disease detection using MRI images.

Alzheimer's disease (AD) is a severe neurological disorder that leads to irreversible memory loss. In the previous research, the early-stage Alzheimer...

Alzheimer's Disease detection and classification using optimized neural network.

Alzheimer's disease (AD) is a degenerative neurological condition characterized by a progressive decline in cognitive abilities, resulting in memory i...

Alzheimer's disease classification using hybrid loss Psi-Net segmentation and a new hybrid network model.

Alzheimer's disease (AD) is a type of brain disorder that is becoming more prevalent worldwide. It is a progressive and irreversible condition that gr...

EEGConvNeXt: A novel convolutional neural network model for automated detection of Alzheimer's Disease and Frontotemporal Dementia using EEG signals.

BACKGROUND AND OBJECTIVE: Deep learning models have gained widespread adoption in healthcare for accurate diagnosis through the analysis of brain sign...

Advancing Alzheimer's disease risk prediction: development and validation of a machine learning-based preclinical screening model in a cross-sectional study.

OBJECTIVES: Alzheimer's disease (AD) poses a significant challenge for individuals aged 65 and older, being the most prevalent form of dementia. Altho...

Analyzing patterns of frequent mental distress in Alzheimer's patients: A generative AI approach.

This study tackles creating Python code for beginners with generative AI and analyzing trends in mental distress among Alzheimer's patients in the US ...

Predicting cognitive decline: Deep-learning reveals subtle brain changes in pre-MCI stage.

BACKGROUND: Mild cognitive impairment (MCI) and preclinical MCI (e.g., subjective cognitive decline, SCD) are considered risk states of dementia, such...

Community Graph Convolution Neural Network for Alzheimer's Disease Classification and Pathogenetic Factors Identification.

As a complex neural network system, the brain regions and genes collaborate to effectively store and transmit information. We abstract the collaborati...

Artificial intelligence-based cardiac transthyretin amyloidosis detection and scoring in scintigraphy imaging: multi-tracer, multi-scanner, and multi-center development and evaluation study.

INTRODUCTION: Providing tools for comprehensively evaluating scintigraphy images could enhance transthyretin amyloid cardiomyopathy (ATTR-CM) diagnosi...

Joint ensemble learning-based risk prediction of Alzheimer's disease among mild cognitive impairment patients.

OBJECTIVE: Due to the recognition for the importance of early intervention in Alzheimer's disease (AD), it is important to focus on prevention and tre...

Detection of Alzheimer Disease in Neuroimages Using Vision Transformers: Systematic Review and Meta-Analysis.

BACKGROUND: Alzheimer disease (AD) is a progressive condition characterized by cognitive decline and memory loss. Vision transformers (ViTs) are emerg...

Identifying natural inhibitors against FUS protein in dementia through machine learning, molecular docking, and dynamics simulation.

Dementia, a complex and debilitating spectrum of neurodegenerative diseases, presents a profound challenge in the quest for effective treatments. The ...