Dementia

A machine learning model for prediction of risk of dementia superimposed on delirium in intensive care patients.

BACKGROUND: The objective of this study was to construct a predictive model using multiple machine learning algorithms to predict the risk of dementia superimposed on delirium (DSD) in dementia patients in the ICU. METHODS: The data for this study were sourced from the Medical Information Mart for Intensive Care IV database. The dataset was divided into a development set (70%) and a test set (30%)...

MRI neuroimaging-based Alzheimer's disease stage classification using deep neural network with convolutional block attention module and GAN-style noise injection.

Millions of individuals worldwide suffer from Alzheimer's disease (AD), a chronic, incurable neurological disorder. For the longevity of people, a computer-aided system can contribute to the maximum possible extent. Recently, Deep-learning algorithms have shown better results than machine learning techniques. Researchers have applied CNN models on MRI datasets in various recent studies and have ob...

Amyloid-β and phosphorylated-tau accumulation in retrosplenial cortex of young Alzheimer's disease model: Impact on network activity.

BackgroundThe retrosplenial cortex (RSC) is a cortical area that functions as a key component of the core network of brain regions involved in cogniti...

Modeling Zero-Dose Children in Ethiopia: A Machine Learning Perspective on Model Performance and Predictor Variables.

BACKGROUND: Despite progress in childhood vaccination, many children in low- and middle-income countries, including Ethiopia, remain unvaccinated, pre...

Developing a Multimodal Screening Algorithm for Mild Cognitive Impairment and Early Dementia in Home Health Care: Protocol for a Cross-Sectional Case-Control Study Using Speech Analysis, Large Language Models, and Electronic Health Records.

BACKGROUND: Mild cognitive impairment and early dementia (MCI-ED) are frequently unrecognized in routine care, particularly in home health care (HHC),...

Multimodal fusion and explainability of artificial intelligence models in Alzheimer's Disease detection.

The integration of multimodal data has emerged as a powerful strategy for enhancing the accuracy and interpretability of artificial intelligence (AI) ...

Multi-dimensional EEG analysis reveals distinct neurophysiological patterns in Alzheimer's and frontotemporal dementia.

AI-driven framework for accurate detection of Alzheimer's disease in EEG.

Alzheimer's Disease (AD) is a rapidly growing neurodegenerative disorder that severely impairs cognitive function, particularly among older adults. Ea...

Investigating the Utility of Explainable Artificial Intelligence for Neuroimaging-Based Dementia Diagnosis and Prognosis.

Artificial intelligence and neuroimaging enable accurate dementia prediction but often involve 'black box' models that can be difficult to trust. Expl...

Biases in an artificial intelligence image-generator's depictions of healthy aging and Alzheimer's.

OBJECTIVE: This content analysis study investigates potential biases in image generation by 2 artificial intelligence (AI) tools, DALL-E 3 and Midjour...

Artificial Intelligence-Assisted Clinical Decision Support System in Telemedical Wound Care: A Randomized Controlled Trial.

BACKGROUND: Chronic wounds are increasingly prevalent due to an aging population and rising chronic diseases. Effective wound care is often hindered b...

Functional and Clinical: An Explainable Deep Learning Model for Multimodal Alzheimer's Disease Classification.

PURPOSE: Functional magnetic resonance imaging (fMRI) and deep learning models can classify Alzheimer's disease (AD) with high accuracy. These models ...

Towards a speech-based digital biomarker for cognitive impairment: speech as a proxy for cognitive assessment.

With the growing prevalence of cognitive decline in ageing populations, accessible and scalable screening tools are essential for early intervention. ...

Smart assistive technologies for neurodisorders: A review on AI, IoT, and wearable systems for enhanced patient care.

Neurological disorders of the brain and spinal cord affect millions of individuals worldwide and continue to rise in prevalence. Conditions such as Al...

Alzheimer's disease diagnosis support for brain perfusion SPECT scans in a real-world clinical cohort.

BackgroundDementia diagnosis is challenging and often delayed. Brain imaging techniques such as single-photon emission computed tomography (SPECT) ima...

The role of dentists in the recognition of neurodegenerative and systemic conditions with neurological involvement.

Dentists are often the first healthcare providers to observe subtle orofacial and behavioral changes that may reflect underlying neurological diseases...

Molecular features of human pathological tau distinguish tauopathy-associated dementias.

In Alzheimer's disease (AD), pathological tau protein shows a progressive accumulation of post-translational modifications (PTMs), reflecting disease ...

Development and Validation of a Machine Learning Model for Dementia Staging in a Heterogeneous Cognitive Impairment Cohort Using Multimodal Features.

RATIONALE AND OBJECTIVES: With the emergence of disease-modifying therapies, precise staging of dementia is urgent. This study aimed to develop a mach...

Towards the development of a management protocol for subjective cognitive decline: Insights from a cross-sectional and longitudinal analysis of multimodal data from a memory clinic.

BackgroundSubjective cognitive decline (SCD) represents the first early symptomatic stage of Alzheimer's disease (AD).ObjectiveWe aimed to investigate...

Ethical Knowledge, Challenges, and Institutional Strategies Among Medical AI Developers and Researchers: Focus Group Study.

BACKGROUND: As artificial intelligence (AI) becomes increasingly embedded in clinical decision-making and preventive care, it is urgent to address eth...