Dementia

AI-based staging, causal hypothesis and progression of subjects at risk of Alzheimer's disease: a multicenter study.

INTRODUCTION: In 2024, 11 European scientific societies/organizations and one patient advocacy association have defined a patient-centered biomarker-based diagnostic workflow for memory clinics evaluating neurocognitive disorders.

Alzheimer's diagnosis by an efficient pipelined gene selection model based on statistical and biological data analysis.

Diagnosing Alzheimer's disease based on gene expression data extracted from microarrays is still an open field of research. Due to the availability of whole-genome data through microarrays technology, diagnosis accuracy is expected to be improved. Despite the high potential of the data prepared by the technology, their analysis on different platforms shows that they may differ for different sample...

Brain metabolic imaging-based model identifies cognitive stability in prodromal Alzheimer's disease.

The recent approval of anti-amyloid pharmaceuticals for the treatment of Alzheimer's disease (AD) has created a pressing need for the ability to accur...

High-Resolution Spatial Profiling of Microglia Reveals Proximity Associated Immunometabolic Reprogramming in Alzheimer's Disease.

Single-cell RNA sequencing has demonstrated that the presence of parenchymal amyloid plaques and intracellular hyperphosphorylated tau pathology is as...

Early detection of Alzheimer's disease progression stages using hybrid of CNN and transformer encoder models.

Alzheimer's disease (AD) is a neurodegenerative disorder that affects memory and cognitive functions. Manual diagnosis is prone to human error, often ...

The two cut-offs approach for plasma p-tau217 in detecting Alzheimer's disease in subjective cognitive decline and mild cognitive impairment.

BACKGROUND: The study aimed to explore the applicability of plasma phosphorylated tau (p-tau)217 in identifying patients with subjective cognitive dec...

Integrating Fine-Tuned LLM with Acoustic Features for Enhanced Detection of Alzheimer's Disease.

Dementia represents a global public health concern, with the early detection of Alzheimer's disease, the most prevalent form of dementia, being of par...

Study on the effect and mechanism of PM on the expression of alzheimer's-like lesions related proteins in SH-SY5Y cells.

Fine particulate matter (PM) is recognized as one of the most harmful environmental pollutants to human health. Current research indicates that PM exh...

Interpretable MRI-Based Deep Learning for Alzheimer's Risk and Progression.

Timely intervention for Alzheimer's disease (AD) requires early detection. The development of immunotherapies targeting amyloid-beta and tau underscor...

EEG-based neurodegenerative disease diagnosis: comparative analysis of conventional methods and deep learning models.

In the context of lifestyle changes, stress and other environmental factors have resulted in the sudden hike in dementia globally. This necessitates i...

Artificial intelligence-driven natural language processing for identifying linguistic patterns in Alzheimer's disease and mild cognitive impairment: A study of lexical, syntactic, and cohesive features of speech through picture description tasks.

BackgroundLanguage deficits often occur early in the neurodegenerative process, yet traditional methods frequently fail to detect subtle changes. Natu...

AD-AutoGPT: An autonomous GPT for Alzheimer's disease infodemiology.

In this pioneering study, inspired by AutoGPT, the state-of-the-art open-source application based on the GPT-4 large language model, we develop a nove...

AI-driven early diagnosis of specific mental disorders: a comprehensive study.

One of the areas where artificial intelligence (AI) technologies are used is the detection and diagnosis of mental disorders. AI approaches, including...

Systematic Identification of Caregivers of Patients Living With Dementia in the Electronic Health Record: Known Contacts and Natural Language Processing Cohort Study.

BACKGROUND: Systemically identifying caregivers in the electronic health record (EHR) is a critical step for delivering patient-centered care, enhanci...

Predicting and Evaluating Cognitive Status in Aging Populations Using Decision Tree Models.

To improve the identification of cognitive impairment by distinguishing normal cognition (NC), mild cognitive impairment (MCI), and Alzheimer's disea...

Use of posterior probabilities from a long short-term memory network for characterizing dance behavior with multiple accelerometers.

BackgroundDancing may be protective for cognitive health among adults with mild cognitive impairment, Alzheimer's disease or dementia; however, additi...

Cognitive impairment screening strategy to reduce the burden of Alzheimer's disease in Shanghai: A system dynamics approach.

BACKGROUND: Population aging increases the economic burden of Alzheimer's disease (AD). Early screening for mild cognitive impairment (MCI) has the po...

Ensemble Learning-Based Alzheimer's Disease Classification Using Electroencephalogram Signals and Clock Drawing Test Images.

Ensemble learning (EL), a machine learning technique that combines the results of multiple learning algorithms to obtain predicted values, aims to ach...

Assessing the Content of Goals of Care Documentation for Hospitalized Patients With Alzheimer's Disease and Related Dementias.

BACKGROUND: Goals of care (GOC) conversations are an evidence-based practice that help clarify and align patient values and preferences for medical ca...

Modality-Aware Discriminative Fusion Network for Integrated Analysis of Brain Imaging Genomics.

Mild cognitive impairment (MCI) represents an early stage of Alzheimer's disease (AD), characterized by subtle clinical symptoms that pose challenges ...