Dementia

A Methodological and Structural Review of Parkinsons Disease Detection Across Diverse Data Modalities

Parkinsons Disease (PD) is a progressive neurological disorder that primarily affects motor functions and can lead to mild cognitive impairment (MCI) and dementia in its advanced stages. With approximately 10 million people diagnosed globally 1 to 1.8 per 1,000 individuals, according to reports by the Japan Times and the Parkinson Foundation early and accurate diagnosis of PD is crucial for impr...

AdCare-VLM: Leveraging Large Vision Language Model (LVLM) to Monitor Long-Term Medication Adherence and Care

Chronic diseases, including diabetes, hypertension, asthma, HIV-AIDS, epilepsy, and tuberculosis, necessitate rigorous adherence to medication to avert disease progression, manage symptoms, and decrease mortality rates. Adherence is frequently undermined by factors including patient behavior, caregiver support, elevated medical costs, and insufficient healthcare infrastructure. We propose AdCare...

Dual-stream algorithms for dementia detection: Harnessing structured and unstructured electronic health record data, a novel approach to prevalence estimation.

INTRODUCTION: Identifying individuals with dementia is crucial for prevalence estimation and service planning, but reliable, scalable methods are lack...

Early detection of Alzheimer's disease using deep learning methods.

INTRODUCTION: Alzheimer's disease (AD), a leading cause of dementia, requires early detection for effective intervention. This study employs AI to ana...

Predicting Progression to Dementia Using Auditory Verbal Learning Test in Community-Dwelling Older Adults Based On Machine Learning.

BACKGROUND: Primary healthcare institutions find identifying individuals with dementia particularly challenging. This study aimed to develop machine l...

Advancing Toward a World Without Vision Loss From Diabetes: Insights From The Mary Tyler Moore Vision Initiative Symposium 2024 on Curing Vision Loss From Diabetes.

The Mary Tyler Moore Vision Initiative (MTM Vision) honors Mary Tyler Moore's commitment to ending vision loss from diabetes. Founded by Moore's husba...

xEEGNet: Towards Explainable AI in EEG Dementia Classification

This work presents xEEGNet, a novel, compact, and explainable neural network for EEG data analysis. It is fully interpretable and reduces overfittin...

Artificial Intelligence for Personalized Prediction of Alzheimer's Disease Progression: A Survey of Methods, Data Challenges, and Future Directions

Alzheimer's Disease (AD) is marked by significant inter-individual variability in its progression, complicating accurate prognosis and personalized ...

HoloDx: Knowledge- and Data-Driven Multimodal Diagnosis of Alzheimer's Disease

Accurate diagnosis of Alzheimer's disease (AD) requires effectively integrating multimodal data and clinical expertise. However, existing methods of...

4D Multimodal Co-attention Fusion Network with Latent Contrastive Alignment for Alzheimer's Diagnosis

Multimodal neuroimaging provides complementary structural and functional insights into both human brain organization and disease-related dynamics. R...

Accelerating Clinical NLP at Scale with a Hybrid Framework with Reduced GPU Demands: A Case Study in Dementia Identification

Clinical natural language processing (NLP) is increasingly in demand in both clinical research and operational practice. However, most of the state-...

Early Detection of Cognitive Impairment in Elderly using a Passive FPVS-EEG BCI and Machine Learning -- Extended Version

Early dementia diagnosis requires biomarkers sensitive to both structural and functional brain changes. While structural neuroimaging biomarkers hav...

WMH-DualTasker: A Weakly Supervised Deep Learning Model for Automated White Matter Hyperintensities Segmentation and Visual Rating Prediction.

White matter hyperintensities (WMH) are neuroimaging markers linked to an elevated risk of cognitive decline. WMH severity is typically assessed via v...

REMEMBER: Retrieval-based Explainable Multimodal Evidence-guided Modeling for Brain Evaluation and Reasoning in Zero- and Few-shot Neurodegenerative Diagnosis

Timely and accurate diagnosis of neurodegenerative disorders, such as Alzheimer's disease, is central to disease management. Existing deep learning ...

The SERENADE project: Sensor-Based Explainable Detection of Cognitive Decline

Mild Cognitive Impairment (MCI) affects 12-18% of individuals over 60. MCI patients exhibit cognitive dysfunctions without significant daily functio...

Examination of the relationship between D-amino acid profiles and cognitive function in individuals with mild cognitive impairment: a machine learning approach.

BACKGROUND: The global prevalence of dementia is significantly increasing. Early detection and prevention strategies, particularly for mild cognitive ...

Applying Data Mining to Predict Perceived Benefits Risks of Robotics at Home for Dementia Caregiving Among African American Families.

We used data mining to predict the attitudes of 527 caregivers towards the pros and cons of using robotics and artificial intelligence (AI) for dement...

The Impact of Artificial Intelligence in Reducing the Cost of Dementia: A Scoping Review.

INTRODUCTION: Dementia is a major cause of disability among the elderly, imposing significant financial burdens on healthcare systems. Traditional car...

Using C4.5 Algorithm to Gain Insights on Stakeholder Engagement and Use of Artificial Intelligence on Social Media in Dementia Caregiving Disparity Research.

We applied machine learning techniques to build models that predict perceived risks and benefits of using artificial intelligence (AI) algorithms to r...

The Pepper Robot in Healthcare: A Scoping Review.

Humanoid robots are increasingly being used in a number of domains. This paper focuses on reviewing the use of the Pepper humanoid robot in healthcare...