Neurology

Diagnosis of Glaucoma on Retinal Fundus Images Using Deep Learning: Detection of Nerve Fiber Layer Defect and Optic Disc Analysis.

Early detection of glaucoma is important to slow down progression of the disease and to prevent total vision loss. Retinal fundus photography is frequently obtained for various eye disease diagnosis and record and is a suitable screening exam for its simplicity and low cost. However, the number of ophthalmologists who are specialized in glaucoma diagnosis is limited. We have been studying automate...

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 patients affected by the Alzheimer's Disease. Further, there are standardized dataset available today that are curated from several centers across the globe that aid in development of Computer Aided Diagnosis tools. However, there are numerous clinical t...

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...

Effects of robot-assisted gait training alongside conventional therapy on the development of walking in children with cerebral palsy.

PURPOSE: To investigate the effects of robot-assisted gait training (RAGT) alongside conventional therapy on the standing and walking abilities of chi...

Lessons learned from conducting a pragmatic, randomized, crossover trial on robot-assisted gait training in children with cerebral palsy (PeLoGAIT).

PURPOSE: To investigate the effectiveness of outpatient robot-assisted gait training (RAGT) in ambulatory children with spastic cerebral palsy.

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...

A natural evolution optimization based deep learning algorithm for neurological disorder classification.

BACKGROUND: A neurological disorder is one of the significant problems of the nervous system that affects the essential functions of the human brain a...

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...

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...

High-accuracy Automated Diagnosis of Parkinson's Disease.

PURPOSE: Parkinson's disease (PD), which is the second most common neurodegenerative disease following Alzheimer's disease, can be diagnosed clinicall...

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.

Electroencephalogram-Based Emotion Recognition Using a Particle Swarm Optimization-Derived Support Vector Machine Classifier.

We sort human emotions using Russell's circumplex model of emotion by classifying electroencephalogram (EEG) signals from 25 subjects into four discre...

[Robotic recovery of walking function in patients in the early recovery period of stroke].

OBJECTIVE: To compare the efficacy of walking function recovery in patients in the early recovery period of ischemic stroke (IS) using an exoskeleton ...

Effect of robot-assisted gait training on motor functions in adolescent and young adult patients with bilateral spastic cerebral palsy: A randomized controlled trial.

BACKGROUND: Robot-assisted gait training (RAGT) allows an intensive gait training in patients with cerebral palsy (CP). There are few evidences on the...

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...