Alzheimer's Disease

An unsupervised learning approach for clustering joint trajectories of Alzheimer's disease biomarkers: An application to ADNI Data.

INTRODUCTION: Current models of Alzheimer's disease (AD) progression assume a common pattern and pathology, oversimplifying the heterogeneity of clinical AD.

Natural language processing-based classification of early Alzheimer's disease from connected speech.

INTRODUCTION: The automated analysis of connected speech using natural language processing (NLP) emerges as a possible biomarker for Alzheimer's disease (AD). However, it remains unclear which types of connected speech are most sensitive and specific for the detection of AD.

Alzheimer's Disease: Exploring Pathophysiological Hypotheses and the Role of Machine Learning in Drug Discovery.

Alzheimer's disease (AD) is a major neurodegenerative dementia, with its complex pathophysiology challenging current treatments. Recent advancements h...

Enhanced detection of mild cognitive impairment in Alzheimer's disease: a hybrid model integrating dual biomarkers and advanced machine learning.

Alzheimer's disease (AD) is a complex, progressive, and irreversible neurodegenerative disorder marked by cognitive decline and memory loss. Early dia...

"Tom and Pepper Lab". Robotics for cognitive stimulation and social skills: A preliminary study.

Dementia affects over 55 million people globally, with early cognitive decline, such as Mild Cognitive Impairment (MCI), often preceding neurodegenera...

Multimodal multiview bilinear graph convolutional network for mild cognitive impairment diagnosis.

Mild cognitive impairment (MCI) is a significant predictor of the early progression of Alzheimer's disease (AD) and can serve as an important indicato...

Palmitoylation-related gene ZDHHC22 as a potential diagnostic and immunomodulatory target in Alzheimer's disease: insights from machine learning analyses and WGCNA.

BACKGROUND: The mechanism of palmitoylation in the pathogenesis of Alzheimer's disease (AD) remains unclear.

Automated karyogram analysis for early detection of genetic and neurodegenerative disorders: a hybrid machine learning approach.

Anomalous chromosomes are the cause of genetic diseases such as cancer, Alzheimer's, Parkinson's, epilepsy, and autism. Karyotype analysis is the stan...

Developing multifactorial dementia prediction models using clinical variables from cohorts in the US and Australia.

Existing dementia prediction models using non-neuroimaging clinical measures have been limited in their ability to identify disease. This study used m...

Machine learning models for dementia screening to classify brain amyloid positivity on positron emission tomography using blood markers and demographic characteristics: a retrospective observational study.

BACKGROUND: Intracerebral amyloid β (Aβ) accumulation is considered the initial observable event in the pathological process of Alzheimer's disease (A...

Sulfonic acid functionalized β-amyloid peptide aggregation inhibitors and antioxidant agents for the treatment of Alzheimer's disease: Combining machine learning, computational, in vitro and in vivo approaches.

Alzheimer's disease (AD) is characterized as a neurodegenerative disorder that is caused by plaque formation by accumulating β-amyloid (Aβ), leading t...

Alzheimer's disease diagnosis using rhythmic power changes and phase differences: a low-density EEG study.

OBJECTIVES: The future emergence of disease-modifying treatments for dementia highlights the urgent need to identify reliable and easily accessible to...

Adapting to evolving MRI data: A transfer learning approach for Alzheimer's disease prediction.

Integrating 3D magnetic resonance imaging (MRI) with machine learning has shown promising results in healthcare, especially in detecting Alzheimer's D...

DeepDrug as an expert guided and AI driven drug repurposing methodology for selecting the lead combination of drugs for Alzheimer's disease.

Alzheimer's Disease (AD) significantly aggravates human dignity and quality of life. While newly approved amyloid immunotherapy has been reported, eff...

Screening of Aβ and phosphorylated tau status in the cerebrospinal fluid through machine learning analysis of portable electroencephalography data.

Diagnosing Alzheimer's disease (AD) through pathological markers is typically costly and invasive. This study aims to find a noninvasive, cost-effecti...

The global research of magnetic resonance imaging in Alzheimer's disease: a bibliometric analysis from 2004 to 2023.

BACKGROUND: Alzheimer's disease (AD) is a common neurodegenerative disorder worldwide and the using of magnetic resonance imaging (MRI) in the managem...

Drug repurposing using artificial intelligence, molecular docking, and hybrid approaches: A comprehensive review in general diseases vs Alzheimer's disease.

BACKGROUND: Alzheimer's disease (AD), the most prevalent form of dementia, remains enigmatic in its origins despite the widely accepted "amyloid hypot...

DML-MFCM: A multimodal fine-grained classification model based on deep metric learning for Alzheimer's disease diagnosis.

BACKGROUND: Alzheimer's disease (AD) is a neurodegenerative disorder. There are no drugs and methods for the treatment of AD, but early intervention c...

Determining causal pathogens and inflammatory state of mastitis in dairy cows via Gram staining of precipitates in milk.

Early detection of bovine mastitis-causing pathogens is necessary for treatment. As culturing methods are time-consuming, a more rapid detection techn...

Generation of high-resolution MPRAGE-like images from 3D head MRI localizer (AutoAlign Head) images using a deep learning-based model.

PURPOSE: Magnetization prepared rapid gradient echo (MPRAGE) is a useful three-dimensional (3D) T1-weighted sequence, but is not a priority in routine...