Dementia

Multi-Task Weakly-Supervised Attention Network for Dementia Status Estimation With Structural MRI.

Accurate prediction of clinical scores (of neuropsychological tests) based on noninvasive structural magnetic resonance imaging (MRI) helps understand the pathological stage of dementia (e.g., Alzheimer's disease (AD)) and forecast its progression. Existing machine/deep learning approaches typically preselect dementia-sensitive brain locations for MRI feature extraction and model construction, pot...

Deep-learning prediction of amyloid deposition from early-phase amyloid positron emission tomography imaging.

OBJECTIVE: While the use of biomarkers for the detection of early and preclinical Alzheimer's Disease has become essential, the need to wait for over an hour after injection to obtain sufficient image quality can be challenging for patients with suspected dementia and their caregivers. This study aimed to develop an image-based deep-learning technique to generate delayed uptake patterns of amyloid...

Methodologies Used to Study the Feasibility, Usability, Efficacy, and Effectiveness of Social Robots For Elderly Adults: Scoping Review.

BACKGROUND: New research fields to design social robots for older people are emerging. By providing support with communication and social interaction,...

Semantic segmentation of human cell nucleus using deep U-Net and other versions of U-Net models.

The deep learning models play an essential role in many areas, including medical image analysis. These models extract important features without human...

Artificial intelligence and machine-learning approaches in structure and ligand-based discovery of drugs affecting central nervous system.

CNS disorders are indications with a very high unmet medical needs, relatively smaller number of available drugs, and a subpar satisfaction level amon...

Automated detection of mild cognitive impairment and dementia from voice recordings: A natural language processing approach.

INTRODUCTION: Automated computational assessment of neuropsychological tests would enable widespread, cost-effective screening for dementia.

Classifying the lifestyle status for Alzheimer's disease from clinical notes using deep learning with weak supervision.

BACKGROUND: Since no effective therapies exist for Alzheimer's disease (AD), prevention has become more critical through lifestyle status changes and ...

Machine learning-based classification of the movements of children with profound or severe intellectual or multiple disabilities using environment data features.

Communication interventions have broadened from dialogical meaning-making, assessment approaches, to remote-controlled interactive objects. Yet, inter...

Multimodal deep learning for Alzheimer's disease dementia assessment.

Worldwide, there are nearly 10 million new cases of dementia annually, of which Alzheimer's disease (AD) is the most common. New measures are needed t...

Machine learning for comprehensive prediction of high risk for Alzheimer's disease based on chromatic pupilloperimetry.

Currently there are no reliable biomarkers for early detection of Alzheimer's disease (AD) at the preclinical stage. This study assessed the pupil lig...

Promoting activity in long-term care facilities with the social robot Pepper: a pilot study.

About 40 000 individuals depend on assisted living in long-term care facilities in Norway. Around 80% of these have a cognitive impairment or suffer f...

Deep convolutional neural networks for automated scoring of pentagon copying test results.

This study aims to investigate the accuracy of a fine-tuned deep convolutional neural network (CNN) for evaluating responses to the pentagon copying t...

Deep learning for Alzheimer's disease diagnosis: A survey.

Alzheimer's Disease (AD) is an irreversible neurodegenerative disease that results in a progressive decline in cognitive abilities. Since AD starts se...

A combination model of AD biomarkers revealed by machine learning precisely predicts Alzheimer's dementia: China Aging and Neurodegenerative Initiative (CANDI) study.

INTRODUCTION: To test the utility of the "A/T/N" system in the Chinese population, we study core Alzheimer's disease (AD) biomarkers in a newly establ...

Neuropsychological test using machine learning for cognitive impairment screening.

OBJECTIVES: Neuropsychological tests (NPTs) are widely used tools to evaluate cognitive functioning. The interpretation of these tests can be time-con...

Determinants of implementing of pet robots in nursing homes for dementia care.

BACKGROUND: Pet robots have been employed as viable substitutes to pet therapy in nursing homes. Despite their potential to enhance the psychosocial h...

A classification for complex imbalanced data in disease screening and early diagnosis.

Imbalanced classification has drawn considerable attention in the statistics and machine learning literature. Typically, traditional classification me...

AD-CovNet: An exploratory analysis using a hybrid deep learning model to handle data imbalance, predict fatality, and risk factors in Alzheimer's patients with COVID-19.

Alzheimer's disease (AD) is the leading cause of dementia globally, with a growing morbidity burden that may exceed diagnosis and management capabilit...

Hippocampal representations for deep learning on Alzheimer's disease.

Deep learning offers a powerful approach for analyzing hippocampal changes in Alzheimer's disease (AD) without relying on handcrafted features. Nevert...

The perspectives of older adults with mild cognitive impairment and their caregivers on the use of socially assistive robots in healthcare: exploring factors that influence attitude in a pre-implementation stage.

BACKGROUND: Due to increasing age and an increasing prevalence rate of neurocognitive disorders such as Mild Cognitive Impairment (MCI) and dementia, ...