Alzheimer's Disease

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 leading to misdiagnosis or delayed detection. MRI techniques help visualize the fine tissues of the brain cells, indicating the stage of disease progression. Artificial intelligence techniques analyze MRI with high accuracy and extract subtle feature...

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 decline (SCD) and mild cognitive impairment (MCI) carrying Alzheimer's disease (AD) pathology in real-world settings.

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...

Neuroprotective Effects of Shrimp Oil Against Oxidative Stress in Chronic Cerebral Hypoperfusion Rats.

Shrimp oil is rich in polyunsaturated fatty acids and astaxanthin, which possesses high antioxidant activity. This study aimed to investigate the impa...

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...

Enhancing Neurodegenerative Disease Diagnosis Through Confidence-Driven Dynamic Spatio-Temporal Convolutional Network.

Dynamic brain networks are more effective than static networks in characterizing the evolving patterns of brain functional connectivity, making them a...

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...

New Targets for Imaging in Nuclear Medicine.

Nuclear medicine is rapidly evolving with new molecular imaging targets and advanced computational tools that promise to enhance diagnostic precision ...

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 ...

How Can Anomalous-Diffusion Neural Networks Under Connectomics Generate Optimized Spatiotemporal Dynamics.

Spatiotemporal dynamics in the brain have been recognized as strongly related to the formation of perceived and cognitive diseases, such as delusions ...