Alzheimer's Disease

Weighted Multi-Modal Contrastive Learning Based Hybrid Network for Alzheimer's Disease Diagnosis.

Multiple imaging modalities and specific proteins in the cerebrospinal fluid, providing a comprehensive understanding of neurodegenerative disorders, have been widely used for computer-aided diagnosis of Alzheimer's disease (AD). Given the proven effectiveness of contrastive learning in aligning multi-modal representation, in this paper, we investigate effective contrastive learning strategies to ...

Reevaluating feature importance in machine learning: concerns regarding SHAP interpretations in the context of the EU artificial intelligence act.

This paper critically examines the analysis conducted by Maußner et al. on AI analysis, particularly their interpretation of feature importances derived from various machine learning models using SHAP (SHapley Additive exPlanations). Although SHAP aids in interpretability, it is subject to model-specific biases that can misrepresent relationships between variables. The paper emphasizes the lack of...

Stages prediction of Alzheimer's disease with shallow 2D and 3D CNNs from intelligently selected neuroimaging data.

Detection of Alzheimer's Disease (AD) is critical for successful diagnosis and treatment, involving the common practice of screening for Mild Cognitiv...

Readability of Online Patient Education Materials for Cleft Care: A Systematic Review and Meta-Analysis.

ObjectiveTo evaluate the readability of online patient education materials (PEMs) for cleft lip and/or palate and assess their alignment with recommen...

Effectiveness of Chatbot interventions for reducing caregiver burden: Protocol for a systematic review and meta-analysis.

This protocol outlines a systematic review and meta-analysis examining the effectiveness of fully automated, AI-driven chatbot interventions in reduci...

Neuropsychological tests and machine learning: identifying predictors of MCI and dementia progression.

BACKGROUND: Early prediction of progression in dementia is of major importance for providing patients with adequate clinical care, with considerable i...

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 (AD...

Intelligent Robot Interventions for People With Dementia: Systematic Review and Meta-Analysis of Randomized Controlled Trials.

BACKGROUND: The application of intelligent robots in therapy is becoming more and more important for people with dementia. More extensive research is ...

Dementia Overdiagnosis in Younger, Higher Educated Individuals Based on MMSE Alone: Analysis Using Deep Learning Technology.

BACKGROUND: Dementia is a multifaceted disorder that affects cognitive function, necessitating accurate diagnosis for effective management and treatme...

Machine learning to detect Alzheimer's disease with data on drugs and diagnoses.

BACKGROUND: Integrating machine learning with medical records offers potential for early detection of Alzheimer's disease (AD), enabling timely interv...

Comparison of Deep Learning and Traditional Machine Learning Models for Predicting Mild Cognitive Impairment Using Plasma Proteomic Biomarkers.

Mild cognitive impairment (MCI) is a clinical condition characterized by a decline in cognitive ability and progression of cognitive impairment. It is...

MCNEL: A multi-scale convolutional network and ensemble learning for Alzheimer's disease diagnosis.

BACKGROUND AND OBJECTIVE: Alzheimer's disease (AD) significantly threatens community well-being and healthcare resource allocation due to its high inc...

Alzheimer's disease prediction using 3D-CNNs: Intelligent processing of neuroimaging data.

Alzheimer's disease (AD) is a severe neurological illness that demolishes memory and brain functioning. This disease affects an individual's capacity ...

A comprehensive approach to anticipating the progression of mild cognitive impairment.

The immersive experience provided by our approach empowers researchers with an intuitive exploration of brain structures. Within the brain's central n...

Deep learning-based classification of dementia using image representation of subcortical signals.

BACKGROUND: Dementia is a neurological syndrome marked by cognitive decline. Alzheimer's disease (AD) and frontotemporal dementia (FTD) are the common...

A Lightweight Deep Convolutional Neural Network Extracting Local and Global Contextual Features for the Classification of Alzheimer's Disease Using Structural MRI.

Recent advancements in the classification of Alzheimer's disease have leveraged the automatic feature generation capability of convolutional neural ne...

AI-Driven Framework for Enhanced and Automated Behavioral Analysis in Morris Water Maze Studies.

The Morris Water Maze (MWM) is a widely used behavioral test to assess the spatial learning and memory of animals, particularly valuable in studying n...

Applying machine learning to high-dimensional proteomics datasets for the identification of Alzheimer's disease biomarkers.

PURPOSE: This study explores the application of machine learning to high-dimensional proteomics datasets for identifying Alzheimer's disease (AD) biom...

Neurofind: using deep learning to make individualised inferences in brain-based disorders.

Within precision psychiatry, there is a growing interest in normative models given their ability to parse heterogeneity. While they are intuitive and ...

Improving ALS detection and cognitive impairment stratification with attention-enhanced deep learning models.

Amyotrophic lateral sclerosis (ALS) is a fatal neurological disease marked by motor deterioration and cognitive decline. Early diagnosis is challengin...