Alzheimer's Disease

Deep learning for subtyping the Alzheimer's disease spectrum.

In a recent article from Cell Reports Medicine, Kwak et al. generate novel insights about subtyping cognitively impaired individuals based on structural imaging. Quantifying heterogeneity in Alzheimer's disease via subtyping could help us harness new disease-modifying therapies and improve patient care by providing a more targeted approach.

Alteration of the corpus callosum in patients with Alzheimer's disease: Deep learning-based assessment.

BACKGROUND: Several studies have reported changes in the corpus callosum (CC) in Alzheimer's disease. However, the involved region differed according to the study population and study group. Using deep learning technology, we ensured accurate analysis of the CC in Alzheimer's disease.

Subtyping of mild cognitive impairment using a deep learning model based on brain atrophy patterns.

Trajectories of cognitive decline vary considerably among individuals with mild cognitive impairment (MCI). To address this heterogeneity, subtyping a...

Deep learning for Alzheimer's disease: Mapping large-scale histological tau protein for neuroimaging biomarker validation.

Abnormal tau inclusions are hallmarks of Alzheimer's disease and predictors of clinical decline. Several tau PET tracers are available for neurodegene...

A whole-process interpretable and multi-modal deep reinforcement learning for diagnosis and analysis of Alzheimer's disease.

. Alzheimer's disease (AD), a common disease of the elderly with unknown etiology, has been adversely affecting many people, especially with the aging...

Identifying and evaluating clinical subtypes of Alzheimer's disease in care electronic health records using unsupervised machine learning.

BACKGROUND: Alzheimer's disease (AD) is a highly heterogeneous disease with diverse trajectories and outcomes observed in clinical populations. Unders...

A deep learning framework identifies dimensional representations of Alzheimer's Disease from brain structure.

Heterogeneity of brain diseases is a challenge for precision diagnosis/prognosis. We describe and validate Smile-GAN (SeMI-supervised cLustEring-Gener...

Performance of Machine Learning Algorithms for Predicting Progression to Dementia in Memory Clinic Patients.

IMPORTANCE: Machine learning algorithms could be used as the basis for clinical decision-making aids to enhance clinical practice.

3D convolutional neural networks for stalled brain capillary detection.

Adequate blood supply is critical for normal brain function. Brain vasculature dysfunctions, including stalled blood flow in cerebral capillaries, are...

Use of robots to encourage social engagement between older adults.

We designed a robotic architecture system within a commercially available socially assistive robot to engage pairs of older adults in multimodal activ...

Feasibility study protocol of the PainChek app to assess the efficacy of a social robot intervention for people with dementia.

AIM: This study aims to test the feasibility of the PainChek app to assess pain for people with dementia living in residential aged care facilities (R...

Embracing the disharmony in medical imaging: A Simple and effective framework for domain adaptation.

Domain shift, the mismatch between training and testing data characteristics, causes significant degradation in the predictive performance in multi-so...

A deep learning framework with an embedded-based feature selection approach for the early detection of the Alzheimer's disease.

Ageing is associated with various ailments including Alzheimer 's disease (AD), which is a progressive form of dementia. AD symptoms develop over a pe...

Deep learning based pipelines for Alzheimer's disease diagnosis: A comparative study and a novel deep-ensemble method.

BACKGROUND: Alzheimer's disease is a chronic neurodegenerative disease that destroys brain cells, causing irreversible degeneration of cognitive funct...

Identification of Alzheimer associated differentially expressed gene through microarray data and transfer learning-based image analysis.

Major factors contribute to mental stress and enhance the progression of late-onset Alzheimer's disease (AD). The factors that lead to neurodegenerati...

Effect of data leakage in brain MRI classification using 2D convolutional neural networks.

In recent years, 2D convolutional neural networks (CNNs) have been extensively used to diagnose neurological diseases from magnetic resonance imaging ...

A Bio-inspired trajectory planning method for robotic manipulators based on improved bacteria foraging optimization algorithm and tau theory.

In this paper, a novel bio-inspired trajectory planning method is proposed for robotic systems based on an improved bacteria foraging optimization alg...

Plasma neurofilament light chain protein is not increased in treatment-resistant schizophrenia and first-degree relatives.

OBJECTIVE: Schizophrenia, a complex psychiatric disorder, is often associated with cognitive, neurological and neuroimaging abnormalities. The process...

Generation of Interconnected Neural Clusters in Multiscale Scaffolds from Human-Induced Pluripotent Stem Cells.

The development of in vitro neural networks depends to a large extent on the scaffold properties, including the scaffold stiffness, porosity, and dime...

Predictive value of ATN biomarker profiles in estimating disease progression in Alzheimer's disease dementia.

We aimed to evaluate the value of ATN biomarker classification system (amyloid beta [A], pathologic tau [T], and neurodegeneration [N]) for predicting...