Neurology

Quantification of Retinal Nerve Fibre Layer Thickness on Optical Coherence Tomography with a Deep Learning Segmentation-Free Approach.

This study describes a segmentation-free deep learning (DL) algorithm for measuring retinal nerve fibre layer (RNFL) thickness on spectral-domain optical coherence tomography (SDOCT). The study included 25,285 B-scans from 1,338 eyes of 706 subjects. Training was done to predict RNFL thickness from raw unsegmented scans using conventional RNFL thickness measurements from good quality images as tar...

An Investigation of Various Machine and Deep Learning Techniques Applied in Automatic Fear Level Detection and Acrophobia Virtual Therapy.

In this paper, we investigate various machine learning classifiers used in our Virtual Reality (VR) system for treating acrophobia. The system automatically estimates fear level based on multimodal sensory data and a self-reported emotion assessment. There are two modalities of expressing fear ratings: the 2-choice scale, where 0 represents relaxation and 1 stands for fear; and the 4-choice scale,...

Machine learning models for identifying preterm infants at risk of cerebral hemorrhage.

Intracerebral hemorrhage in preterm infants is a major cause of brain damage and cerebral palsy. The pathogenesis of cerebral hemorrhage is multifacto...

Expression of Cytokines and Chemokines as Predictors of Stroke Outcomes in Acute Ischemic Stroke.

Ischemic stroke remains one of the most debilitating diseases and is the fifth leading cause of death in the US. The ability to predict stroke outcom...

Predicting Chronic Subdural Hematoma Recurrence and Stroke Outcomes While Withholding Antiplatelet and Anticoagulant Agents.

The aging of the western population and the increased use of oral anticoagulation (OAC) and antiplatelet drugs (APD) will result in a clinical dilemm...

Integrated Hypothalamic Transcriptome Profiling Reveals the Reproductive Roles of mRNAs and miRNAs in Sheep.

Early studies have provided a wealth of information on the functions of microRNAs (miRNAs). However, less is known regarding their functions in the hy...

Robotic-assisted hand therapy for improvement of hand function in children with cerebral palsy: a case series study.

BACKGROUND: Most types of robot-assisted training (RT) have been used in Cerebral Palsy (CP) patients only focus on proximal upper extremity. Few of s...

A bioinspired analogous nerve towards artificial intelligence.

A bionic artificial device commonly integrates various distributed functional units to mimic the functions of biological sensory neural system, bringi...

Precision Medicine and Artificial Intelligence: A Pilot Study on Deep Learning for Hypoglycemic Events Detection based on ECG.

Tracking the fluctuations in blood glucose levels is important for healthy subjects and crucial diabetic patients. Tight glucose monitoring reduces th...

Learning a cortical parcellation of the brain robust to the MRI segmentation with convolutional neural networks.

The parcellation of the human cortex into meaningful anatomical units is a common step of various neuroimaging studies. There have been multiple succe...

EEG-Controlled Wall-Crawling Cleaning Robot Using SSVEP-Based Brain-Computer Interface.

The assistive, adaptive, and rehabilitative applications of EEG-based robot control and navigation are undergoing a major transformation in dimension ...

Comparison of different input modalities and network structures for deep learning-based seizure detection.

The manual review of an electroencephalogram (EEG) for seizure detection is a laborious and error-prone process. Thus, automated seizure detection bas...

Robotic orthoses for gait rehabilitation: An overview of mechanical design and control strategies.

The application of robotic devices in providing physiotherapies to post-stroke patients and people suffering from incomplete spinal cord injuries is r...

Adaptive sparse learning using multi-template for neurodegenerative disease diagnosis.

Neurodegenerative diseases are excessively affecting millions of patients, especially elderly people. Early detection and management of these diseases...

Endocannabinoid degradation inhibitors ameliorate neuronal and synaptic alterations following traumatic brain injury.

Our previous work showed that lateral fluid percussion injury to the sensorimotor cortex (SMC) of anesthetized rats increased neuronal synaptic hypere...

Assessing stroke severity using electronic health record data: a machine learning approach.

BACKGROUND: Stroke severity is an important predictor of patient outcomes and is commonly measured with the National Institutes of Health Stroke Scale...

Automatic classification of gait patterns in children with cerebral palsy using fuzzy clustering method.

BACKGROUND: Subjective classification of gait pattern in children with cerebral palsy depends on the assessor's experience, while mathematical methods...

Assessing various sensorimotor and cognitive functions in people with epilepsy is feasible with robotics.

BACKGROUND: Epilepsy is a common neurological disorder characterized by recurrent seizures, along with comorbid cognitive and psychosocial impairment....

Machine learning methods for optimal prediction of motor outcome in Parkinson's disease.

PURPOSE: It is vital to appropriately power clinical trials towards discovery of novel disease-modifying therapies for Parkinson's disease (PD). Thus,...

Fatigue Evaluation through Machine Learning and a Global Fatigue Descriptor.

Research in physiology and sports science has shown that fatigue, a complex psychophysiological phenomenon, has a relevant impact in performance and i...