Geriatrics

Machine Learning Predictive Models Can Improve Efficacy of Clinical Trials for Alzheimer's Disease.

BACKGROUND: The ideal participants for Alzheimer's disease (AD) clinical trials would show cognitive decline in the absence of treatment (i.e., placebo arm) and also would be responsive to the therapeutic intervention being studied (i.e., drug arm). One strategy to boost the power of trials is to enroll individuals who are more likely to progress targeted using data-driven predictive models.

Convolutional Neural Network-based MR Image Analysis for Alzheimer's Disease Classification.

BACKGROUND: In this study, we used a convolutional neural network (CNN) to classify Alzheimer's disease (AD), mild cognitive impairment (MCI), and normal control (NC) subjects based on images of the hippocampus region extracted from magnetic resonance (MR) images of the brain.

Application and Development of Artificial Intelligence and Intelligent Disease Diagnosis.

With the continuous development of artificial intelligence (AI) technology, big data-supported AI technology with considerable computer and learning c...

A Machine Learning Framework for Assessment of Cognitive and Functional Impairments in Alzheimer's Disease: Data Preprocessing and Analysis.

The neuropsychological scores and Functional Activities Questionnaire (FAQ) are significant to measure the cognitive and functional domain of the pati...

CT-Based Quantitative Analysis for Pathological Features Associated With Postoperative Recurrence and Potential Application Upon Artificial Intelligence: A Narrative Review With a Focus on Chronic Subdural Hematomas.

Chronic subdural hematomas (CSDHs) frequently affect the elderly population. The postoperative recurrence rate of CSDHs is high, ranging from 3% to 20...

Using Machine Learning to Predict Dementia from Neuropsychiatric Symptom and Neuroimaging Data.

BACKGROUND: Machine learning (ML) is a promising technique for patient-specific prediction of mild cognitive impairment (MCI) and dementia development...

Predicting Cognitive Impairment and Dementia: A Machine Learning Approach.

BACKGROUND: Efforts to identify important risk factors for cognitive impairment and dementia have to date mostly relied on meta-analytic strategies. A...

Integrating Convolutional Neural Networks and Multi-Task Dictionary Learning for Cognitive Decline Prediction with Longitudinal Images.

BACKGROUND: Disease progression prediction based on neuroimaging biomarkers is vital in Alzheimer's disease (AD) research. Convolutional neural networ...

Classification of Alzheimer's Disease with Respect to Physiological Aging with Innovative EEG Biomarkers in a Machine Learning Implementation.

BACKGROUND: Several studies investigated clinical and instrumental differences to make diagnosis of dementia in general and in Alzheimer's disease (AD...

Gene Ontology Curation of Neuroinflammation Biology Improves the Interpretation of Alzheimer's Disease Gene Expression Data.

BACKGROUND: Gene Ontology (GO) is a major bioinformatic resource used for analysis of large biomedical datasets, for example from genome-wide associat...

Multivariate Data Analysis and Machine Learning for Prediction of MCI-to-AD Conversion.

There has always been a need for discovering efficient and dependable Alzheimer's disease (AD) diagnostic biomarkers. Like the majority of diseases, t...

Robotic Systems Involved in the Diagnosis of Neurodegenerative Diseases.

The continuing development of robotics on the one hand and, on the other hand, the estimated relative growth in the number of elderly individuals suff...

PTML Modeling for Alzheimer's Disease: Design and Prediction of Virtual Multi-Target Inhibitors of GSK3B, HDAC1, and HDAC6.

BACKGROUND: Alzheimer's disease is characterized by a progressive pattern of cognitive and functional impairment, which ultimately leads to death. Com...

Effects of innovative hip-knee-ankle interlimb coordinated robot training on ambulation, cardiopulmonary function, depression, and fall confidence in acute hemiplegia.

BACKGROUND: While Walkbot-assisted locomotor training (WLT) provided ample evidence on balance and gait improvements, the therapeutic effects on cardi...

Addressing the Ethics of Telepresence Applications Through End-User Engagement.

Portacolone et al.'s Ethics Review highlights the ethical challenges associated with the implementation of telepresence devices and applications in th...

Evaluation and Prediction of Early Alzheimer's Disease Using a Machine Learning-based Optimized Combination-Feature Set on Gray Matter Volume and Quantitative Susceptibility Mapping.

BACKGROUND: Because Alzheimer's Disease (AD) has very complicated pattern changes, it is difficult to evaluate it with a specific factor. Recently, no...

Diagnosis of Osteoporosis using modified U-net architecture with attention unit in DEXA and X-ray images.

BACKGROUND: Osteoporosis, a silent killing disease of fracture risk, is normally determined based on the bone mineral density (BMD) and T-score values...

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.

[The current status of elderly care robots].

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 researc...