Neurology

Applications of deep learning techniques for automated multiple sclerosis detection using magnetic resonance imaging: A review.

Multiple Sclerosis (MS) is a type of brain disease which causes visual, sensory, and motor problems for people with a detrimental effect on the functioning of the nervous system. In order to diagnose MS, multiple screening methods have been proposed so far; among them, magnetic resonance imaging (MRI) has received considerable attention among physicians. MRI modalities provide physicians with fund...

A deep learning based ensemble learning method for epileptic seizure prediction.

In epilepsy, patients suffer from seizures which cannot be controlled with medicines or surgical treatments in more than 30% of the cases. Prediction of epileptic seizures is extremely important so that they can be controlled with medication before they actually occur. Researchers have proposed multiple machine/deep learning based methods to predict epileptic seizures; however, accurate prediction...

Implementation of a Deep Learning Algorithm Based on Vertical Ground Reaction Force Time-Frequency Features for the Detection and Severity Classification of Parkinson's Disease.

Conventional approaches to diagnosing Parkinson's disease (PD) and rating its severity level are based on medical specialists' clinical assessment of ...

The Emerging Role of Long Non-Coding RNAs and MicroRNAs in Neurodegenerative Diseases: A Perspective of Machine Learning.

Neurodegenerative diseases (NDs) are characterized by progressive neuronal dysfunction and death of brain cells population. As the early manifestation...

Application of deep-learning to the seronegative side of the NMO spectrum.

OBJECTIVES: To apply a deep-learning algorithm to brain MRIs of seronegative patients with neuromyelitis optica spectrum disorders (NMOSD) and NMOSD-l...

Multimodal Machine Learning Using Visual Fields and Peripapillary Circular OCT Scans in Detection of Glaucomatous Optic Neuropathy.

PURPOSE: To develop and validate a multimodal artificial intelligence algorithm, FusionNet, using the pattern deviation probability plots from visual ...

A 3D deep learning model to predict the diagnosis of dementia with Lewy bodies, Alzheimer's disease, and mild cognitive impairment using brain 18F-FDG PET.

PURPOSE: The purpose of this study is to develop and validate a 3D deep learning model that predicts the final clinical diagnosis of Alzheimer's disea...

S3Reg: Superfast Spherical Surface Registration Based on Deep Learning.

Cortical surface registration is an essential step and prerequisite for surface-based neuroimaging analysis. It aligns cortical surfaces across indivi...

Discovery of Parkinson's disease states and disease progression modelling: a longitudinal data study using machine learning.

BACKGROUND: Parkinson's disease is heterogeneous in symptom presentation and progression. Increased understanding of both aspects can enable better pa...

Affective Computing on Machine Learning-Based Emotion Recognition Using a Self-Made EEG Device.

In this research, we develop an affective computing method based on machine learning for emotion recognition using a wireless protocol and a wearable ...

Neuroinflammation and Alzheimer's Disease: A Machine Learning Approach to CSF Proteomics.

In Alzheimer's disease (AD), the contribution of pathophysiological mechanisms other than amyloidosis and tauopathy is now widely recognized, although...

Early balance impairment in Parkinson's Disease: Evidence from Robot-assisted axial rotations.

OBJECTIVE: Early postural instability (PI) is a red flag for the diagnosis of Parkinson's disease (PD). Several patients, however, fall within the fir...

A robot goes to rehab: a novel gamified system for long-term stroke rehabilitation using a socially assistive robot-methodology and usability testing.

BACKGROUND: Socially assistive robots (SARs) have been proposed as a tool to help individuals who have had a stroke to perform their exercise during t...

The Effect of Applying Robot-Assisted Task-Oriented Training Using Human-Robot Collaborative Interaction Force Control Technology on Upper Limb Function in Stroke Patients: Preliminary Findings.

Stroke is one of the leading causes of death and the primary cause of acquired disability worldwide. Many stroke survivors have difficulty using their...

Robot-assisted Exploration of Somatic Nerves in the Pelvis and Transection of the Sacrospinous Ligament for Alcock Canal Syndrome.

STUDY OBJECTIVE: Some articles have reported the surgical management of Alcock canal syndrome (ACS) using the transperineal [1], transgluteal [2], or ...

A dual-channel language decoding from brain activity with progressive transfer training.

When we view a scene, the visual cortex extracts and processes visual information in the scene through various kinds of neural activities. Previous st...

Guidelines for Conducting Ethical Artificial Intelligence Research in Neurology: A Systematic Approach for Clinicians and Researchers.

Preemptive recognition of the ethical implications of study design and algorithm choices in artificial intelligence (AI) research is an important but ...

EEG-Based Emotion Recognition by Convolutional Neural Network with Multi-Scale Kernels.

Besides facial or gesture-based emotion recognition, Electroencephalogram (EEG) data have been drawing attention thanks to their capability in counter...

FLDNet: Frame-Level Distilling Neural Network for EEG Emotion Recognition.

Based on the current research on EEG emotion recognition, there are some limitations, such as hand-engineered features, redundant and meaningless sign...

Predicting Recurrence for Patients With Ischemic Cerebrovascular Events Based on Process Discovery and Transfer Learning.

The recurrence of Ischemic cerebrovascular events (ICE) often results in a high rate of mortality and disability. However, due to the lack of labeled ...