Alzheimer's Disease

Restoration of amyloid PET images obtained with short-time data using a generative adversarial networks framework.

Our purpose in this study is to evaluate the clinical feasibility of deep-learning techniques for F-18 florbetaben (FBB) positron emission tomography (PET) image reconstruction using data acquired in a short time. We reconstructed raw FBB PET data of 294 patients acquired for 20 and 2 min into standard-time scanning PET (PET) and short-time scanning PET (PET) images. We generated a standard-time s...

Translating amyloid PET of different radiotracers by a deep generative model for interchangeability.

It is challenging to compare amyloid PET images obtained with different radiotracers. Here, we introduce a new approach to improve the interchangeability of amyloid PET acquired with different radiotracers through image-level translation. Deep generative networks were developed using unpaired PET datasets, consisting of 203 [C]PIB and 850 [F]florbetapir brain PET images. Using 15 paired PET datase...

Plasma d-glutamate levels for detecting mild cognitive impairment and Alzheimer's disease: Machine learning approaches.

BACKGROUND: d-glutamate, which is involved in N-methyl-d-aspartate receptor modulation, may be associated with cognitive ageing.

Machine learning identifies candidates for drug repurposing in Alzheimer's disease.

Clinical trials of novel therapeutics for Alzheimer's Disease (AD) have consumed a large amount of time and resources with largely negative results. R...

A 3D densely connected convolution neural network with connection-wise attention mechanism for Alzheimer's disease classification.

PURPOSE: Alzheimer's disease (AD) is a progressive and irreversible neurodegenerative disease. In recent years, machine learning methods have been wid...

A Low-Cost Three-Dimensional DenseNet Neural Network for Alzheimer's Disease Early Discovery.

Alzheimer's disease is the most prevalent dementia among the elderly population. Early detection is critical because it can help with future planning ...

A Correlation Analysis between SNPs and ROIs of Alzheimer's Disease Based on Deep Learning.

. At present, the research methods for image genetics of Alzheimer's disease based on machine learning are mainly divided into three steps: the first ...

Deep learning-based T1-enhanced selection of linear attenuation coefficients (DL-TESLA) for PET/MR attenuation correction in dementia neuroimaging.

PURPOSE: The accuracy of existing PET/MR attenuation correction (AC) has been limited by a lack of correlation between MR signal and tissue electron d...

Barriers and facilitators to the implementation of social robots for older adults and people with dementia: a scoping review protocol.

BACKGROUND: Psychosocial health issues such as depression and social isolation are an important cause of morbidity and premature mortality for older a...

An anatomical knowledge-based MRI deep learning pipeline for white matter hyperintensity quantification associated with cognitive impairment.

Recent studies have confirmed that white matter hyperintensities (WMHs) accumulated in strategic brain regions can predict cognitive impairments assoc...

A multilayer multimodal detection and prediction model based on explainable artificial intelligence for Alzheimer's disease.

Alzheimer's disease (AD) is the most common type of dementia. Its diagnosis and progression detection have been intensively studied. Nevertheless, res...

Screening of Alzheimer's disease by facial complexion using artificial intelligence.

Despite the increasing incidence and high morbidity associated with dementia, a simple, non-invasive, and inexpensive method of screening for dementia...

Brain Asymmetry Detection and Machine Learning Classification for Diagnosis of Early Dementia.

Early identification of degenerative processes in the human brain is considered essential for providing proper care and treatment. This may involve de...

Cognitive and MRI trajectories for prediction of Alzheimer's disease.

The concept of Mild Cognitive Impairment (MCI) is used to describe the early stages of Alzheimer's disease (AD), and identification and treatment befo...

Modeling autosomal dominant Alzheimer's disease with machine learning.

INTRODUCTION: Machine learning models were used to discover novel disease trajectories for autosomal dominant Alzheimer's disease.

True ultra-low-dose amyloid PET/MRI enhanced with deep learning for clinical interpretation.

PURPOSE: While sampled or short-frame realizations have shown the potential power of deep learning to reduce radiation dose for PET images, evidence i...

Modular machine learning for Alzheimer's disease classification from retinal vasculature.

Alzheimer's disease is the leading cause of dementia. The long progression period in Alzheimer's disease provides a possibility for patients to get ea...

Improved amyloid burden quantification with nonspecific estimates using deep learning.

PURPOSE: Standardized uptake value ratio (SUVr) used to quantify amyloid-β burden from amyloid-PET scans can be biased by variations in the tracer's n...

Computer-Aided Diagnosis of Alzheimer's Disease through Weak Supervision Deep Learning Framework with Attention Mechanism.

Alzheimer's disease (AD) is the most prevalent neurodegenerative disease causing dementia and poses significant health risks to middle-aged and elderl...

Deep learning detection of informative features in tau PET for Alzheimer's disease classification.

BACKGROUND: Alzheimer's disease (AD) is the most common type of dementia, typically characterized by memory loss followed by progressive cognitive dec...