Latest AI and machine learning research in dementia for healthcare professionals.
The detection of Alzheimer disease (AD) from clinical MRI data is an active area of research in medical imaging. Recent advances in quantum computing, particularly the integration of parameterized quantum circuits (PQCs) with classical machine learning architectures, offer new opportunities to develop models that may outperform traditional methods. However, quantum machine learning (QML) remains...
Background:Speech patterns have emerged as potential diagnostic markers for conditions with varying etiologies. Machine learning (ML) presents an opportunity to harness these patterns for accurate disease diagnosis. Objective: This review synthesized findings from studies exploring ML's capability in leveraging speech for the diagnosis of neurological, laryngeal and mental disorders. Methods...
Accurately discriminating progressive stages of Alzheimer's Disease (AD) is crucial for early diagnosis and prevention. It often involves multiple i...
Characterizing a preclinical stage of Alzheimer's Disease (AD) via single imaging is difficult as its early symptoms are quite subtle. Therefore, ma...
Stacking excessive layers in DNN results in highly underdetermined system when training samples are limited, which is very common in medical applica...
Dementia, a progressive neurodegenerative disorder, affects memory, reasoning, and daily functioning, creating challenges for individuals and health...
Early diagnosis of Alzheimer's disease (AD) is critical for intervention before irreversible neurodegeneration occurs. Structural MRI (sMRI) is wide...
Alzheimer's disease (AD) diagnosis is complex, requiring the integration of imaging and clinical data for accurate assessment. While deep learning h...
Alzheimer's disease (AD) is the most prevalent form of dementia, characterized by progressive memory loss and cognitive decline, often affecting behav...
INTRODUCTION: The utility of retinal photography-derived aging biomarkers for predicting cognitive decline remains under-explored.
BACKGROUND: Integrating non-invasive measures to estimate abnormal amyloid beta accumulation (Aβ+) is key to developing a screening tool for preclinic...
Gene regulatory networks (GRNs) provide a global representation of how genetic/genomic information is transferred in living systems and are a key comp...
Dementia is a progressive cognitive syndrome with Alzheimer's disease (AD) as the leading cause. Conversation-based AD detection offers a cost-effec...
Alzheimer's disease (AD) classification models usually segment the entire brain image into voxel blocks and assign them labels consistent with the ent...
Spontaneous brain activity generically displays transient spatiotemporal coherent structures, which can selectively be affected in various neurologi...
Early diagnosis of Alzheimer's Disease (AD) faces multiple data-related challenges, including high variability in patient data, limited access to sp...
Urinary tract infections (UTIs) are a significant health concern, particularly for people living with dementia (PLWD), as they can lead to severe co...
This study examines the potential causal relationship between head injury and the risk of developing Alzheimer's disease (AD) using Bayesian network...
The role of ketone bodies in Alzheimers disease (AD) remains incompletely understood, particularly regarding their influence on amyloid pathology. W...
This study presents the development and testing of a conversational speech system designed for robots to detect speech biomarkers indicative of cogn...