Neurology

Dementia

Latest AI and machine learning research in dementia for healthcare professionals.

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The clinical significance of an AI-based assumption model for neurocognitive diseases using a novel dual-task system.

Dual-task composed of gait or stepping tasks combined with cognitive tasks has been well-established...

Alzheimer's Disease Dementia Prevalence in the United States: A County-Level Spatial Machine Learning Analysis.

A growing body of literature has examined the impact of neighborhood characteristics on Alzheimer's ...

Computational modelling for risk assessment of neurological disorder in diabetes using Hodgkin-Huxley model.

BACKGROUND: Diabetes mellitus, characterized by chronic glucose dysregulation, significantly increas...

Transcriptomic analyses of human brains with Alzheimer's disease identified dysregulated epilepsy-causing genes.

BACKGROUND & OBJECTIVE: Alzheimer's Disease (AD) patients at multiple stages of disease progression ...

Using machine learning and electronic health record (EHR) data for the early prediction of Alzheimer's Disease and Related Dementias.

BACKGROUND: Over 6 million patients in the United States are affected by Alzheimer's Disease and Rel...

c-Triadem: A constrained, explainable deep learning model to identify novel biomarkers in Alzheimer's disease.

Alzheimer's disease (AD) is a neurodegenerative disorder that requires early diagnosis for effective...

Hybrid of DSR-GAN and CNN for Alzheimer disease detection based on MRI images.

In this paper, we propose a deep super-resolution generative adversarial network (DSR-GAN) combined ...

Neuroimaging-derived biological brain age and its associations with glial reactivity and synaptic dysfunction cerebrospinal fluid biomarkers.

Magnetic resonance Imaging (MRI)-derived brain-age prediction is a promising biomarker of biological...

The future of Alzheimer's disease risk prediction: a systematic review.

BACKGROUND: Alzheimer's disease is the most prevalent kind of age-associated dementia among older ad...

Automated phenotyping of mild cognitive impairment and Alzheimer's disease and related dementias using electronic health records.

OBJECTIVES: Unstructured and structured data in electronic health records (EHR) are a rich source of...

IT: An interpretable transformer model for Alzheimer's disease prediction based on PET/MR images.

Alzheimer's disease (AD) represents a significant challenge due to its progressive neurodegenerative...

CLIC1 and IFITM2 expression in brain tissue correlates with cognitive impairment via immune dysregulation in sepsis and Alzheimer's disease.

BACKGROUND: Sepsis, a life-threatening condition driven by dysregulated host responses to infection,...

Natural language processing models reveal neural dynamics of human conversation.

Through conversation, humans engage in a complex process of alternating speech production and compre...

The Cognivue Amyloid Risk Measure (CARM): A Novel Method to Predict the Presence of Amyloid with Cognivue Clarity.

INTRODUCTION: At the present time, clinical detection of individuals who have amyloid in their brain...

Ensemble deep learning for Alzheimer's disease diagnosis using MRI: Integrating features from VGG16, MobileNet, and InceptionResNetV2 models.

Alzheimer's disease (AD) is a neurodegenerative disorder characterized by the accumulation of amyloi...

The impact of Alzheimer's disease on cortical complexity and its underlying biological mechanisms.

BACKGROUND: Alzheimer's disease (AD) might impact the complexity of cerebral cortex, and the underly...

A fine-tuned convolutional neural network model for accurate Alzheimer's disease classification.

Alzheimer's disease (AD) is one of the primary causes of dementia in the older population, affecting...

DEMENTIA: A Hybrid Attention-Based Multimodal and Multi-Task Learning Framework With Expert Knowledge for Alzheimer's Disease Assessment From Speech.

The prevalence of Alzheimer's disease (AD) is rising annually, imposing a severe burden on patients ...

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