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Alzheimer Disease

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

NeuroImage
Integrating 3D magnetic resonance imaging (MRI) with machine learning has shown promising results in healthcare, especially in detecting Alzheimer's Disease (AD). However, changes in MRI technologies and acquisition protocols often yield limited data...

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

Scientific reports
Diagnosing Alzheimer's disease (AD) through pathological markers is typically costly and invasive. This study aims to find a noninvasive, cost-effective method using portable electroencephalography (EEG) to detect changes in AD-related biomarkers in ...

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

Scientific reports
Alzheimer's Disease (AD) significantly aggravates human dignity and quality of life. While newly approved amyloid immunotherapy has been reported, effective AD drugs remain to be identified. Here, we propose a novel AI-driven drug-repurposing method,...

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

Computer methods and programs in biomedicine
BACKGROUND: Alzheimer's disease (AD), the most prevalent form of dementia, remains enigmatic in its origins despite the widely accepted "amyloid hypothesis," which implicates amyloid-beta peptide aggregates in its pathogenesis and progression. Despit...

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

Journal of X-ray science and technology
BACKGROUND: Alzheimer's disease (AD) is a neurodegenerative disorder. There are no drugs and methods for the treatment of AD, but early intervention can delay the deterioration of the disease. Therefore, the early diagnosis of AD and mild cognitive i...

Clinical validation of artificial intelligence-based single-subject morphometry without normative reference database.

Journal of Alzheimer's disease : JAD
BACKGROUND: Single-subject voxel-based morphometry (VBM) is a powerful technique for reader-independent detection of brain atrophy in structural magnetic resonance imaging (MRI) to support the (differential) diagnosis and staging of neurodegenerative...

Predicting cognitive decline from neuropsychiatric symptoms and Alzheimer's disease biomarkers: A machine learning approach to a population-based data.

Journal of Alzheimer's disease : JAD
BACKGROUND: The aim of this study was to examine the potential added value of including neuropsychiatric symptoms (NPS) in machine learning (ML) models, along with demographic features and Alzheimer's disease (AD) biomarkers, to predict decline or no...

Designing a Multimodal and Culturally Relevant Alzheimer Disease and Related Dementia Generative Artificial Intelligence Tool for Black American Informal Caregivers: Cognitive Walk-Through Usability Study.

JMIR aging
BACKGROUND: Many members of Black American communities, faced with the high prevalence of Alzheimer disease and related dementias (ADRD) within their demographic, find themselves taking on the role of informal caregivers. Despite being the primary in...

Automated Idiopathic Normal Pressure Hydrocephalus Diagnosis via Artificial Intelligence-Based 3D T1 MRI Volumetric Analysis.

AJNR. American journal of neuroradiology
BACKGROUND AND PURPOSE: Idiopathic normal pressure hydrocephalus (iNPH) is reversible dementia that is underdiagnosed. The purpose of this study was to develop an automated diagnostic method for iNPH using artificial intelligence techniques with a T1...