Dementia

Automatic lesion detection and segmentation in F-flutemetamol positron emission tomography images using deep learning.

BACKGROUND: Beta amyloid in the brain, which was originally confirmed by post-mortem examinations, can now be confirmed in living patients using amyloid positron emission tomography (PET) tracers, and the accuracy of diagnosis can be improved by beta amyloid plaque confirmation in patients. Amyloid deposition in the brain is often associated with the expression of dementia. Hence, it is important ...

Deep learning-based speech analysis for Alzheimer's disease detection: a literature review.

BACKGROUND: Alzheimer's disease has become one of the most common neurodegenerative diseases worldwide, which seriously affects the health of the elderly. Early detection and intervention are the most effective prevention methods currently. Compared with traditional detection methods such as traditional scale tests, electroencephalograms, and magnetic resonance imaging, speech analysis is more con...

The usability and feasibility validation of the social robot MINI in people with dementia and mild cognitive impairment; a study protocol.

BACKGROUND: Social robots have demonstrated promising outcomes in terms of increasing the social health and well-being of people with dementia and mil...

VGG-TSwinformer: Transformer-based deep learning model for early Alzheimer's disease prediction.

BACKGROUND AND OBJECTIVE: Mild cognitive impairment (MCI) is a transitional state between normal aging and Alzheimer's disease (AD), and accurately pr...

Dementia Detection from Speech Using Machine Learning and Deep Learning Architectures.

Dementia affects the patient's memory and leads to language impairment. Research has demonstrated that speech and language deterioration is often a cl...

Interpretable brain disease classification and relevance-guided deep learning.

Deep neural networks are increasingly used for neurological disease classification by MRI, but the networks' decisions are not easily interpretable by...

Dialogue agents for artificial intelligence-based conversational systems for cognitively disabled: a systematic review.

PURPOSE: We present a systematic literature review of dialogue agents for Artificial Intelligence (AI) and agent-based conversational systems dealing ...

Generative adversarial network constrained multiple loss autoencoder: A deep learning-based individual atrophy detection for Alzheimer's disease and mild cognitive impairment.

Exploring individual brain atrophy patterns is of great value in precision medicine for Alzheimer's disease (AD) and mild cognitive impairment (MCI). ...

Zoom-In Neural Network Deep-Learning Model for Alzheimer's Disease Assessments.

Deep neural networks have been successfully applied to generate predictive patterns from medical and diagnostic data. This paper presents an approach ...

GeneSelectML: a comprehensive way of gene selection for RNA-Seq data via machine learning algorithms.

Selection of differentially expressed genes (DEGs) is a vital process to discover the causes of diseases. It has been shown that modelling of genomics...

Multi-stage classification of Alzheimer's disease from F-FDG-PET images using deep learning techniques.

The study aims to implement a convolutional neural network framework that uses the 18F-FDG PET modality of brain imaging to detect multiple stages of ...

Deep learning signature of brain [F]FDG PET associated with cognitive outcome of rapid eye movement sleep behavior disorder.

An objective biomarker to predict the outcome of isolated rapid eye movement sleep behavior disorder (iRBD) is crucial for the management. This study ...

Multi-label classification of Alzheimer's disease stages from resting-state fMRI-based correlation connectivity data and deep learning.

Alzheimer's disease is a neurodegenerative condition that gradually impairs cognitive abilities. Recently, various neuroimaging modalities and machine...

Strategies to Implement Pet Robots in Long-Term Care Facilities for Dementia Care: A Modified Delphi Study.

OBJECTIVES: Pet robots are technology-based substitutes for live animals that have demonstrated psychosocial benefits for people living with dementia ...

Early diagnosis of Alzheimer's disease and mild cognitive impairment based on electroencephalography: From the perspective of event related potentials and deep learning.

Alzheimer's disease (AD), a neurodegenerative disorder characterized by progressive cognitive decline, is generally prevalent in elderly people with s...

Deep learning-based diagnosis of Alzheimer's disease using brain magnetic resonance images: an empirical study.

The limited accessibility of medical specialists for Alzheimer's disease (AD) can make obtaining an accurate diagnosis in a timely manner challenging ...

Brain Age Prediction/Classification through Recurrent Deep Learning with Electroencephalogram Recordings of Seizure Subjects.

With modern population growth and an increase in the average lifespan, more patients are becoming afflicted with neurodegenerative diseases such as de...

Functional magnetic resonance imaging, deep learning, and Alzheimer's disease: A systematic review.

Alzheimer's disease (AD) is currently diagnosed using a mixture of psychological tests and clinical observations. However, these diagnoses are not per...

Generalizable deep learning model for early Alzheimer's disease detection from structural MRIs.

Early diagnosis of Alzheimer's disease plays a pivotal role in patient care and clinical trials. In this study, we have developed a new approach based...

Identifying the presence and severity of dementia by applying interpretable machine learning techniques on structured clinical records.

BACKGROUND: Dementia develops as cognitive abilities deteriorate, and early detection is critical for effective preventive interventions. However, mai...