Alzheimer's Disease

Propagating Uncertainty Across Cascaded Medical Imaging Tasks for Improved Deep Learning Inference.

Although deep networks have been shown to perform very well on a variety of medical imaging tasks, inference in the presence of pathology presents several challenges to common models. These challenges impede the integration of deep learning models into real clinical workflows, where the customary process of cascading deterministic outputs from a sequence of image-based inference steps (e.g. regist...

Investigating Simultaneity for Deep Learning-Enhanced Actual Ultra-Low-Dose Amyloid PET/MR Imaging.

BACKGROUND AND PURPOSE: Diagnostic-quality amyloid PET images can be created with deep learning using actual ultra-low-dose PET images and simultaneous structural MR imaging. Here, we investigated whether simultaneity is required; if not, MR imaging-assisted ultra-low-dose PET imaging could be performed with separate PET/CT and MR imaging acquisitions.

Machine Learning-Assisted Pattern Recognition of Amyloid Beta Aggregates with Fluorescent Conjugated Polymers and Graphite Oxide Electrostatic Complexes.

Five fluorescent positively charged poly(-aryleneethynylene) (-) were designed to construct electrostatic complexes - with negatively charged graphene...

Intelligent assistive technology devices for persons with dementia: A scoping review.

Assistive technology (AT) with context-aware computing and artificial intelligence capabilities can be applied to address cognitive and communication ...

DOTA: Deep Learning Optimal Transport Approach to Advance Drug Repositioning for Alzheimer's Disease.

Alzheimer's disease (AD) is the leading cause of age-related dementia, affecting over 5 million people in the United States and incurring a substantia...

An Exploration: Alzheimer's Disease Classification Based on Convolutional Neural Network.

Alzheimer's disease (AD) is the most generally known neurodegenerative disorder, leading to a steady deterioration in cognitive ability. Deep learning...

Comparing two artificial intelligence software packages for normative brain volumetry in memory clinic imaging.

PURPOSE: To compare two artificial intelligence software packages performing normative brain volumetry and explore whether they could differently impa...

Application of artificial intelligence in brain molecular imaging.

Initial development of artificial Intelligence (AI) and machine learning (ML) dates back to the mid-twentieth century. A growing awareness of the pote...

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 structur...

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 ...

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...

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...