Alzheimer's Disease

Validation of Random Forest Machine Learning Models to Predict Dementia-Related Neuropsychiatric Symptoms in Real-World Data.

BACKGROUND: Neuropsychiatric symptoms (NPS) are the leading cause of the social burden of dementia but their role is underestimated.

Utility of MemTrax and Machine Learning Modeling in Classification of Mild Cognitive Impairment.

BACKGROUND: The widespread incidence and prevalence of Alzheimer's disease and mild cognitive impairment (MCI) has prompted an urgent call for research to validate early detection cognitive screening and assessment.

A Community-Based Study Identifying Metabolic Biomarkers of Mild Cognitive Impairment and Alzheimer's Disease Using Artificial Intelligence and Machine Learning.

BACKGROUND: Currently, there is no objective, clinically available tool for the accurate diagnosis of Alzheimer's disease (AD). There is a pressing ne...

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. Conseque...

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 determin...

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...

The Use of Random Forests to Classify Amyloid Brain PET.

PURPOSE: To evaluate random forests (RFs) as a supervised machine learning algorithm to classify amyloid brain PET as positive or negative for amyloid...

Prediction of amyloid aggregation rates by machine learning and feature selection.

A novel data-based machine learning algorithm for predicting amyloid aggregation rates is reported in this paper. Based on a highly nonlinear projecti...

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...

Impact of Data Presentation on Physician Performance Utilizing Artificial Intelligence-Based Computer-Aided Diagnosis and Decision Support Systems.

Ultrasound (US) is a valuable imaging modality used to detect primary breast malignancy. However, radiologists have a limited ability to distinguish b...

Transfer learning on T1-weighted images for brain age estimation.

Due to both the hidden nature and the irreversibility of Alzheimers disease (AD), it has become the killer of the elderly and is thus the focus of muc...

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...