Alzheimer's Disease

Machine learning analyses of antibody somatic mutations predict immunoglobulin light chain toxicity.

In systemic light chain amyloidosis (AL), pathogenic monoclonal immunoglobulin light chains (LC) form toxic aggregates and amyloid fibrils in target organs. Prompt diagnosis is crucial to avoid permanent organ damage, but delayed diagnosis is common because symptoms usually appear only after strong organ involvement. Here we present LICTOR, a machine learning approach predicting LC toxicity in AL,...

Prediction of caregiver quality of life in amyotrophic lateral sclerosis using explainable machine learning.

Amyotrophic Lateral Sclerosis (ALS) is a rare neurodegenerative, fatal and currently incurable disease. People with ALS need support from informal caregivers due to the motor and cognitive decline caused by the disease. This study aims to identify caregivers whose quality of life (QoL) may be impacted as a result of caring for a person with ALS. In this study, we worked towards the identification ...

Barriers and facilitators to the implementation of social robots for older adults and people with dementia: a scoping review.

BACKGROUND: Psychosocial issues, such as social isolation and loneliness among older adults and people with dementia, continue to pose challenges with...

Effectiveness of robot therapy in the management of behavioural and psychological symptoms for individuals with dementia: A systematic review and meta-analysis.

Robot therapy presents a promising alternative in dementia care. However, its effectiveness has not been verified comprehensively. This systematic rev...

Pivotal Response Treatment with and without robot-assistance for children with autism: a randomized controlled trial.

Pivotal response treatment (PRT) is a promising intervention focused on improving social communication skills in children with autism spectrum disorde...

MILAMP: Multiple Instance Prediction of Amyloid Proteins.

Amyloid proteins are implicated in several diseases such as Parkinson's, Alzheimer's, prion diseases, etc. In order to characterize the amyloidogenici...

Predicting the Prognosis of MCI Patients Using Longitudinal MRI Data.

The aim of this study is to develop a computer-aided diagnosis system with a deep-learning approach for distinguishing "Mild Cognitive Impairment (MCI...

Cortical Thickness from MRI to Predict Conversion from Mild Cognitive Impairment to Dementia in Parkinson Disease: A Machine Learning-based Model.

Background Group comparison results associating cortical thinning and Parkinson disease (PD) dementia (PDD) are limited in their application to clinic...

Application of deep learning to understand resilience to Alzheimer's disease pathology.

People who have Alzheimer's disease neuropathologic change (ADNC) typically associated with dementia but not the associated cognitive decline can be c...

Artificial intelligence extension of the OSCAR-IB criteria.

Artificial intelligence (AI)-based diagnostic algorithms have achieved ambitious aims through automated image pattern recognition. For neurological di...

Acceptability of Social Robots and Adaptation of Hybrid-Face Robot for Dementia Care in India: A Qualitative Study.

OBJECTIVES: This study aims to understand the acceptability of social robots and the adaptation of the Hybrid-Face Robot for dementia care in India.

Deep recurrent model for individualized prediction of Alzheimer's disease progression.

Alzheimer's disease (AD) is known as one of the major causes of dementia and is characterized by slow progression over several years, with no treatmen...

A machine learning model for identifying patients at risk for wild-type transthyretin amyloid cardiomyopathy.

Transthyretin amyloid cardiomyopathy, an often unrecognized cause of heart failure, is now treatable with a transthyretin stabilizer. It is therefore ...

A Deep Learning Strategy for Automatic Sleep Staging Based on Two-Channel EEG Headband Data.

Sleep disturbances are common in Alzheimer's disease and other neurodegenerative disorders, and together represent a potential therapeutic target for ...

Texture analysis of muscle MRI: machine learning-based classifications in idiopathic inflammatory myopathies.

To develop a machine learning (ML) model that predicts disease groups or autoantibodies in patients with idiopathic inflammatory myopathies (IIMs) usi...

A machine learning approach to screen for preclinical Alzheimer's disease.

Combining multimodal biomarkers could help in the early diagnosis of Alzheimer's disease (AD). We included 304 cognitively normal individuals from the...

White matter hyperintensities segmentation using the ensemble U-Net with multi-scale highlighting foregrounds.

White matter hyperintensities (WMHs) are abnormal signals within the white matter region on the human brain MRI and have been associated with aging pr...

A Pilot Study to Detect Agitation in People Living with Dementia Using Multi-Modal Sensors.

People living with dementia (PLwD) often exhibit behavioral and psychological symptoms, such as episodes of agitation and aggression. Agitated behavio...

Artificial intelligence in neurodegenerative diseases: A review of available tools with a focus on machine learning techniques.

Neurodegenerative diseases have shown an increasing incidence in the older population in recent years. A significant amount of research has been condu...

Humanoid socially assistive robots in dementia care: a qualitative study about expectations of caregivers and dementia trainers.

OBJECTIVE: To examine the expectations of informal caregivers, nurses, and dementia trainers regarding the support of (physical and psychosocial) huma...