Alzheimer's Disease

"She Had a Smile on Her Face as Wide as the Great Australian Bite": A Qualitative Examination of Family Perceptions of a Therapeutic Robot and a Plush Toy.

BACKGROUND AND OBJECTIVES: Recent years have seen social robotic pets introduced as a means of treating behavioral and psychological symptoms of dementia, and many show promising potential. In this study, we sought to explore family members' perceptions of the Japanese-developed baby harp seal, Paro (version 9), and a look-alike, nonrobotic Plush Toy, when used by their relative with dementia for ...

Machine Learning Algorithm Helps Identify Non-Diagnosed Prodromal Alzheimer's Disease Patients in the General Population.

BACKGROUND: Recruiting patients for clinical trials of potential therapies for Alzheimer's disease (AD) remains a major challenge, with demand for trial participants at an all-time high. The AD treatment R and D pipeline includes around 112 agents. In the United States alone, 150 clinical trials are seeking 70,000 participants. Most people with early cognitive impairment consult primary care provi...

Episodic-Memory Performance in Machine Learning Modeling for Predicting Cognitive Health Status Classification.

BACKGROUND: Memory dysfunction is characteristic of aging and often attributed to Alzheimer's disease (AD). An easily administered tool for preliminar...

Detecting Alzheimer's Disease from Continuous Speech Using Language Models.

BACKGROUND: Recently, many studies have been carried out to detect Alzheimer's disease (AD) from continuous speech by linguistic analysis and modeling...

Improving PET Imaging Acquisition and Analysis With Machine Learning: A Narrative Review With Focus on Alzheimer's Disease and Oncology.

Machine learning (ML) algorithms have found increasing utility in the medical imaging field and numerous applications in the analysis of digital bioma...

Optimizing Machine Learning Methods to Improve Predictive Models of Alzheimer's Disease.

BACKGROUND: Predicting clinical course of cognitive decline can boost clinical trials' power and improve our clinical decision-making. Machine learnin...

Screening for Alzheimer's Disease Using Saliva: A New Approach Based on Machine Learning and Raman Hyperspectroscopy.

BACKGROUND: Alzheimer's disease and related dementias (ADRDs) are being diagnosed at epidemic rates, with incidence to triple from 35 to 115 million c...

Volumetric Histogram-Based Alzheimer's Disease Detection Using Support Vector Machine.

In this research work, machine learning techniques are used to classify magnetic resonance imaging brain scans of people with Alzheimer's disease. Thi...

Alzheimer's Disease Classification Based on Multi-feature Fusion.

BACKGROUND: In this study, we investigated the fusion of texture and morphometric features as a possible diagnostic biomarker for Alzheimer's Disease ...

Deep Learning for Alzheimer's Disease Classification using Texture Features.

BACKGROUND: We propose a classification method for Alzheimer's disease (AD) based on the texture of the hippocampus, which is the organ that is most a...

Social Robots as a Complementary Therapy in Chronic, Progressive Diseases.

Globally, the world population is ageing, which increases the prevalence of non-communicable diseases that affect patients both physically and psychol...

Multi-Modality Cascaded Convolutional Neural Networks for Alzheimer's Disease Diagnosis.

Accurate and early diagnosis of Alzheimer's disease (AD) plays important role for patient care and development of future treatment. Structural and fun...

[Big data and artificial intelligence for diagnostic decision support in atypical dementia].

The differential diagnosis of atypical dementia remains difficult. The use of positron emission tomography (PET) still represents the gold standard fo...

Detection of Early Stage Alzheimer's Disease using EEG Relative Power with Deep Neural Network.

Electroencephalogram (EEG) signal based early diagnosis of Alzheimer's Disease (AD), especially a discrimination between healthy control (HC) and mild...

Isometry Invariant Shape Descriptors for Abnormality Detection on Brain Surfaces Affected by Alzheimer's Disease.

Alzheimer's disease (AD), a progressive brain disorder, is the most common neurodegenerative disease in older adults. There is a need for brain struct...

A Clinically-Translatable Machine Learning Algorithm for the Prediction of Alzheimer's Disease Conversion in Individuals with Mild and Premild Cognitive Impairment.

BACKGROUND: Available therapies for Alzheimer's disease (AD) can only alleviate and delay the advance of symptoms, with the greatest impact eventually...

Network pharmacology-based analysis of Chinese herbal Naodesheng formula for application to Alzheimer's disease.

Naodesheng (NDS) formula, which consists of Rhizoma Chuanxiong, Lobed Kudzuvine, Carthamus tinctorius, Radix Notoginseng, and Crataegus pinnatifida, i...

Data Mining and Machine Learning Methods for Dementia Research.

Patient data in clinical research often includes large amounts of structured information, such as neuroimaging data, neuropsychological test results, ...

Development of Teaching Materials of Dementia by Incorporating a Communications Robot for Schoolchildren Who Are Dementia Supporters.

In this study, we aimed to teach elementary school students how to practically deal with elderly people with dementia and developed and evaluated teac...

Machine Learning, Sentiment Analysis, and Tweets: An Examination of Alzheimer's Disease Stigma on Twitter.

OBJECTIVES: Social scientists need practical methods for harnessing large, publicly available datasets that inform the social context of aging. We des...