Dementia

An Ensemble Learning Artificial Intelligence Model for Alzheimer's Disease Detection Using OCT.

PURPOSE: There has been significant progress in detecting Alzheimer's disease (AD) using retinal imaging. We developed an ensemble learning-based deep learning (DL) model, integrating different inputs from OCT for the detection of AD-dementia and early AD. DESIGN: A retrospective multicenter case-control study. PARTICIPANTS: A total of 190 participants with AD-dementia and 623 cognitively normal c...

A multi-target therapeutic framework for Alzheimer's disease: an integrative mechanistic review.

BACKGROUND: Alzheimer's disease (AD) is increasingly recognized as a multifactorial network disorder in which amyloid and tau pathology interact with mitochondrial dysfunction, neuroinflammation, metabolic impairment, vascular dysregulation, and synaptic failure. This review provides an integrative, systems-level synthesis of these mechanisms with emphasis on diagnostic and therapeutic implication...

Hippocampus-centered structural covariance network reorganization in Alzheimer's disease: An individualized graph-based biomarker validated by machine learning.

Alzheimer's disease (AD) is characterized by progressive brain network disintegration, yet quantifying this process at an individual level remains cha...

MRI-to-PET synthesis via deep learning for amyloid-β quantification in Alzheimer's disease.

OBJECTIVES: Amyloid-β (Aβ) PET is crucial for diagnosing and monitoring Alzheimer's disease (AD), but its high cost and radiation exposure limit its u...

Latent diffusion autoencoders: Toward efficient and meaningful unsupervised representation learning in medical imaging - a case study on Alzheimer's disease.

This study presents Latent Diffusion Autoencoder (LDAE), a novel encoder-decoder diffusion-based framework for efficient and meaningful unsupervised l...

PyCaret machine learning library with three preprocessing steps after eLORETA source estimation predicts Alzheimer's disease.

Alzheimer's disease (AD) -the most common form of dementia- begins with mild memory loss and gradually progresses, eventually resulting in a generaliz...

Early diagnosis of Alzheimer's disease based on brain morphological changes: A comprehensive approach combining voxel-based morphometry and deep learning.

Deep learning algorithms optimize data by enhancing resolution and suppressing noise associated with biological knowledge. The root issue is that, for...

Leveraging hemispheric asymmetry in structural MRI with an attention-guided 3D CNN for early prediction of Alzheimer's conversion.

Early identification of mild cognitive impairment (MCI) progressing to Alzheimer's disease (AD) is of paramount importance. Despite the notable advanc...

Synergistic integration of clinical and multi-omics data for early MCI diagnosis using an attention-based graph fusion network.

BACKGROUND: Mild cognitive impairment (MCI), a precursor to Alzheimer's disease (AD), requires precise early diagnosis. Single-omics approaches often ...

Multi-omics integration identifies CACNG3 as a synaptic calcium channel regulator in Alzheimer's disease: Mechanistic insights and therapeutic implications.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline, memory impairment, and impaired daily functio...

Plasma metabolomics identifies lipid mediators linking depression and cognitive decline in late-life depression.

BACKGROUND: Late-life depression (LLD) features recurrent episodes and frequently co-exists with cognitive impairment, which predicts worse outcomes a...

Longitudinal changes in subcortical functional connectivity during Alzheimer's disease progression.

Human cognition and behavior rely on the integration of large-scale neural networks that connect the cerebral cortex and subcortical structures. Emerg...

Dysfunctional resting state network connectivity predicts postoperative delirium after major surgery.

BACKGROUND: Postoperative delirium is associated with increased morbidity, mortality, future cognitive decline, or dementia. Understanding the neural ...

Humane Intelligence in Geropsychiatric Care: Relational Artificial Intelligence, Clinical Wisdom, and the Moral Grid Operational Index.

Artificial intelligence (AI) already influences how older adults are identified for services, supported between provider visits, and referred for care...

Artificial Intelligence and Its Application in the Oral Health Care for People with Disabilities: Toward Inclusive and Equitable Innovation.

This article introduces prosocial artificial intelligence (AI) as a transformative framework for addressing oral health disparities among people with ...

Machine Learning-Enhanced Hyperspectral Raman Imaging for Label-Free Molecular Atlas of Alzheimer's Brain.

Label-free molecular imaging that enables the construction of a molecular atlas of biological tissues is vital for understanding complex physiological...

Deep learning-based cognitive impairment brain imaging analysis: New methods, new technologies, and new paradigms.

Cognitive impairment arising from ischemic stroke, Alzheimer's disease, and Parkinson's disease presents distinct structural and network-level alterat...

Early Alzheimer's disease classification via structure and feature-based graph attention network from multi-center data.

With the increasing Alzheimer's disease (AD) prevalence, morbidity, and mortality, its early diagnosis is particularly important. The multi-modal data...

Smiling difficulties in Alzheimer's disease linked to reduced nucleus accumbens and pallidum brain volume: Deep learning insights.

Patients tend to lose the ability to smile during the course of dementia. However, such impairments have rarely been reported, likely due to challenge...

Neurodevelopmental disabilities among children and adolescents: perspectives and priorities in low- and middle-income countries.

PURPOSE OF REVIEW: To synthesize recent evidence on neurodevelopmental disorders (NDDs) among children and adolescents in low- and middle-income count...