Neurology

Data augmentation for deep-learning-based electroencephalography.

BACKGROUND: Data augmentation (DA) has recently been demonstrated to achieve considerable performance gains for deep learning (DL)-increased accuracy and stability and reduced overfitting. Some electroencephalography (EEG) tasks suffer from low samples-to-features ratio, severely reducing DL effectiveness. DA with DL thus holds transformative promise for EEG processing, possibly like DL revolution...

Deep learning for cerebral angiography segmentation from non-contrast computed tomography.

Cerebral computed tomography angiography is a widely available imaging technique that helps in the diagnosis of vascular pathologies. Contrast administration is needed to accurately assess the arteries. On non-contrast computed tomography, arteries are hardly distinguishable from the brain tissue, therefore, radiologists do not consider this imaging modality appropriate for the evaluation of vascu...

Changes in technology acceptance among older people with dementia: the role of social robot engagement.

OBJECTIVE: Emerging technologies such as social robots have shown to be effective in reducing loneliness and agitation for older people with dementia....

Functional fine-mapping of noncoding risk variants in amyotrophic lateral sclerosis utilizing convolutional neural network.

Recent large-scale genome-wide association studies have identified common genetic variations that may contribute to the risk of amyotrophic lateral sc...

Improving abnormal gait patterns by using a gait exercise assist robot (GEAR) in chronic stroke subjects: A randomized, controlled, pilot trial.

BACKGROUND: Although the Gait Exercise Assist Robot (GEAR) has been reported to effectively improve gait of hemiplegic patients, no study has investig...

Robot-assisted Nerve Plane-sparing Eradication of Deep Endometriosis with Double-bipolar Method.

OBJECTIVE: To demonstrate anatomic and technical highlights of a robot-assisted nerve plane-sparing eradication of deep endometriosis (DE).

Machine Learning for Brain Stroke: A Review.

Machine Learning (ML) delivers an accurate and quick prediction outcome and it has become a powerful tool in health settings, offering personalized cl...

Auto-segmentation of organs at risk for head and neck radiotherapy planning: From atlas-based to deep learning methods.

Radiotherapy (RT) is one of the basic treatment modalities for cancer of the head and neck (H&N), which requires a precise spatial description of the ...

Intra-subject approach for gait-event prediction by neural network interpretation of EMG signals.

BACKGROUND: Machine learning models were satisfactorily implemented for estimating gait events from surface electromyographic (sEMG) signals during wa...

Changes in functional connectivity after theta-burst transcranial magnetic stimulation for post-traumatic stress disorder: a machine-learning study.

Intermittent theta burst stimulation (iTBS) is a novel treatment approach for post-traumatic stress disorder (PTSD), and recent neuroimaging work indi...

Genes, the brain, and artificial intelligence in evolution.

Three important systems, genes, the brain, and artificial intelligence (especially deep learning) have similar goals, namely, the maximization of like...

Inexpensive, non-invasive biomarkers predict Alzheimer transition using machine learning analysis of the Alzheimer's Disease Neuroimaging (ADNI) database.

The Alzheimer's Disease Neuroimaging (ADNI) database is an expansive undertaking by government, academia, and industry to pool resources and data on s...

Major depressive disorder diagnosis based on effective connectivity in EEG signals: a convolutional neural network and long short-term memory approach.

Deep learning techniques have recently made considerable advances in the field of artificial intelligence. These methodologies can assist psychologist...

Machine learning and wearable devices of the future.

Machine learning (ML) is increasingly recognized as a useful tool in healthcare applications, including epilepsy. One of the most important applicatio...

Robotic tests for position sense and movement discrimination in the upper limb reveal that they each are highly reproducible but not correlated in healthy individuals.

BACKGROUND: Robotic technologies for neurological assessment provide sensitive, objective measures of behavioural impairments associated with injuries...

Effects of a Humanoid Companion Robot on Dementia Symptoms and Caregiver Distress for Residents in Long-Term Care.

OBJECTIVES: To test the effects of deploying a humanoid companion robot (Kabochan) in comparison with usual care for long-term care facilities' reside...

Alzheimer's Disease stage identification using deep learning models.

OBJECTIVE: The aim of this research is to identify the stage of Alzheimer's Disease (AD) patients through the use of mobility data and deep learning m...

Parkinson's disease detection from 20-step walking tests using inertial sensors of a smartphone: Machine learning approach based on an observational case-control study.

Parkinson's disease (PD) is a neurodegenerative disease inducing dystrophy of the motor system. Automatic movement analysis systems have potential in ...

XGBoost, a Machine Learning Method, Predicts Neurological Recovery in Patients with Cervical Spinal Cord Injury.

The accurate prediction of neurological outcomes in patients with cervical spinal cord injury (SCI) is difficult because of heterogeneity in patient c...

A Review of Deep Learning for Screening, Diagnosis, and Detection of Glaucoma Progression.

UNLABELLED: Because of recent advances in computing technology and the availability of large datasets, deep learning has risen to the forefront of art...