Alzheimer's Disease

Digital Gaming Interventions in Psychiatry: Evidence, Applications and Challenges.

Human evolution has regularly intersected with technology. Digitalization of various services has brought a paradigm shift in consumerism. Treading this path, mental health practice has gradually moved to Digital Mental Health Interventions (DMHI), to improve service access and delivery. Applied games are one such innovation that has gained recent popularity in psychiatry. Based on the principles ...

A comparison of machine learning methods for survival analysis of high-dimensional clinical data for dementia prediction.

Data collected from clinical trials and cohort studies, such as dementia studies, are often high-dimensional, censored, heterogeneous and contain missing information, presenting challenges to traditional statistical analysis. There is an urgent need for methods that can overcome these challenges to model this complex data. At present there is no cure for dementia and no treatment that can successf...

The effect of PARO robotic seals for hospitalized patients with dementia: A feasibility study.

Robotic seals have been studied in long-term care settings; though, no studies of patients with dementia in the acute care setting have been reported....

The Perceptions of People with Dementia and Key Stakeholders Regarding the Use and Impact of the Social Robot MARIO.

People with dementia often experience loneliness and social isolation. This can result in increased cognitive decline which, in turn, has a negative i...

PredAmyl-MLP: Prediction of Amyloid Proteins Using Multilayer Perceptron.

Amyloid is generally an aggregate of insoluble fibrin; its abnormal deposition is the pathogenic mechanism of various diseases, such as Alzheimer's di...

On the use of AI for Generation of Functional Music to Improve Mental Health.

Increasingly music has been shown to have both physical and mental health benefits including improvements in cardiovascular health, a link to reductio...

Identification of early mild cognitive impairment using multi-modal data and graph convolutional networks.

BACKGROUND: The identification of early mild cognitive impairment (EMCI), which is an early stage of Alzheimer's disease (AD) and is associated with b...

Use of Patient-Reported Symptoms from an Online Symptom Tracking Tool for Dementia Severity Staging: Development and Validation of a Machine Learning Approach.

BACKGROUND: SymptomGuide Dementia (DGI Clinical Inc) is a publicly available online symptom tracking tool to support caregivers of persons living with...

Development and Validation of a Deep Learning-Based Automatic Brain Segmentation and Classification Algorithm for Alzheimer Disease Using 3D T1-Weighted Volumetric Images.

BACKGROUND AND PURPOSE: Limited evidence has suggested that a deep learning automatic brain segmentation and classification method, based on T1-weight...

Alzheimer's diagnosis using deep learning in segmenting and classifying 3D brain MR images.

BACKGROUND AND OBJECTIVES: Dementia is one of the brain diseases with serious symptoms such as memory loss, and thinking problems. According to the Wo...

Prediction of amyloid β PET positivity using machine learning in patients with suspected cerebral amyloid angiopathy markers.

Amyloid-β(Aβ) PET positivity in patients with suspected cerebral amyloid angiopathy (CAA) MRI markers is predictive of a worse cognitive trajectory, a...

Attenuation correction using deep Learning and integrated UTE/multi-echo Dixon sequence: evaluation in amyloid and tau PET imaging.

PURPOSE: PET measures of amyloid and tau pathologies are powerful biomarkers for the diagnosis and monitoring of Alzheimer's disease (AD). Because cor...

Internet of Things and Machine Learning for Healthy Ageing: Identifying the Early Signs of Dementia.

Identifying the symptoms of the early stages of dementia is a difficult task, particularly for older adults living in residential care. Internet of Th...

Using Machine Learning to Predict Suicide Attempts in Military Personnel.

Identifying predictors of suicide attempts is critical in intervention and prevention efforts, yet finding predictors has proven difficult due to the ...

Testing a convolutional neural network-based hippocampal segmentation method in a stroke population.

As stroke mortality rates decrease, there has been a surge of effort to study poststroke dementia (PSD) to improve long-term quality of life for strok...

Stacked autoencoders as new models for an accurate Alzheimer's disease classification support using resting-state EEG and MRI measurements.

OBJECTIVE: This retrospective and exploratory study tested the accuracy of artificial neural networks (ANNs) at detecting Alzheimer's disease patients...

Automated detection of cerebral microbleeds in MR images: A two-stage deep learning approach.

Cerebral Microbleeds (CMBs) are small chronic brain hemorrhages, which have been considered as diagnostic indicators for different cerebrovascular dis...

Prediction of 7-year's conversion from subjective cognitive decline to mild cognitive impairment.

Subjective cognitive decline (SCD) is a high-risk yet less understood status before developing Alzheimer's disease (AD). This work included 76 SCD ind...

Detection of Mild Cognitive Impairment Through Natural Language and Touchscreen Typing Processing.

Mild cognitive impairment (MCI), an identified prodromal stage of Alzheimer's Disease (AD), often evades detection in the early stages of the conditio...

A Comparative Analysis of Hybrid Deep Learning Models for Human Activity Recognition.

Recent advances in artificial intelligence and machine learning (ML) led to effective methods and tools for analyzing the human behavior. Human Activi...