Dementia

Artificial Intelligence, Speech, and Language Processing Approaches to Monitoring Alzheimer's Disease: A Systematic Review.

BACKGROUND: Language is a valuable source of clinical information in Alzheimer's disease, as it declines concurrently with neurodegeneration. Consequently, speech and language data have been extensively studied in connection with its diagnosis.

Vitamin D in mild cognitive impairment and Alzheimer's disease. A study in older Greek adults.

BACKGROUND: In recent years, accumulating evidence has linked vitamin D deficiency to cognitive dysfunction and dementia. This study aimed at determining the relevance of serum 25-hydroxyvitamin D concentrations in mild cognitive impairment (MCI) and Alzheimer's disease (AD) in older Greek adults. It also examined whether the vitamin D level could be considered a predisposing factor for conversion...

Successful Resuscitation of a Young Girl Who Drank Rivastigmine With Respiratory Failure.

Rivastigmine is a non-competitive reversible inhibitor of acetylcholinesterase which is approved as one of the fi rst-line treatment options for Alzhe...

[Early prognosis of Alzheimer's disease based on convolutional neural networks and ensemble learning].

Alzheimer's disease (AD) is a typical neurodegenerative disease, which is clinically manifested as amnesia, loss of language ability and self-care abi...

An artificial neural network model for clinical score prediction in Alzheimer disease using structural neuroimaging measures.

BACKGROUND: The development of diagnostic and prognostic tools for Alzheimer disease is complicated by substantial clinical heterogeneity in prodromal...

Evaluation of a Companion Robot for Individuals With Dementia: Quantitative Findings of the MARIO Project in an Irish Residential Care Setting.

The current study focuses on the short-term effect of MARIO, a social robot, on quality of life, depression, and perceived social support in persons w...

[Artificial Intelligence for Diagnostic Support of Neuroimage].

Artificial intelligence (AI) shows promises in terms of diagnostic support on neuroimaging. We developed a software that predicts Alzheimer's disease ...

[Use AI for Dementia Diagnosis].

Using AI for dementia diagnosis is still in research stage, however some of the work points to the development of new medical devices. One is a voxel-...

[Support for Patients and Their Family Using Artificial Intelligence].

For patients with dementia and their family, artificial intelligence (AI) has been utilized to support diagnosis and evaluation. In addition, communic...

Alzheimer's Disease Brain Network Classification Using Improved Transfer Feature Learning with Joint Distribution Adaptation.

Alzheimer's disease significantly affects the quality of life of patients. This paper proposes an approach to identify Alzheimer's disease based on tr...

Multimodal Data Fusion of Deep Learning and Dynamic Functional Connectivity Features to Predict Alzheimer's Disease Progression.

Early prediction of diseased brain conditions is critical for curing illness and preventing irreversible neuronal dysfunction and loss. Generically re...

Fused Group Lasso Regularized Multi-Task Feature Learning and Its Application to the Cognitive Performance Prediction of Alzheimer's Disease.

Alzheimer's disease (AD) is characterized by gradual neurodegeneration and loss of brain function, especially for memory during early stages. Regressi...

The Residual Center of Mass: An Image Descriptor for the Diagnosis of Alzheimer Disease.

A crucial quest in neuroimaging is the discovery of image features (biomarkers) associated with neurodegenerative disorders. Recent works show that su...

"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 demen...

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 tri...

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...