Neurology

Robot controlled, continuous passive movement of the ankle reduces spinal cord excitability in participants with spasticity: a pilot study.

Spasticity of the ankle reduces quality of life by impeding walking and other activities of daily living. Robot-driven continuous passive movement (CPM) is a strategy for lower limb spasticity management but effects on spasticity, walking ability and spinal cord excitability (SCE) are unknown. The objectives of this experiment were to evaluate (1) acute changes in SCE induced by 30 min of CPM at t...

Deep Granular Feature-Label Distribution Learning for Neuroimaging-based Infant Age Prediction.

Neuroimaging-based infant age prediction is important for brain development analysis but often suffers insufficient data. To address this challenge, we introduce label distribution learning (LDL), a popular machine learning paradigm focusing on the small sample problem, for infant age prediction. As directly applying LDL yields dramatically increased number of day-to-day age labels and also extrem...

Acute ischemic stroke lesion core segmentation in CT perfusion images using fully convolutional neural networks.

The use of Computed Tomography (CT) imaging for patients with stroke symptoms is an essential step for triaging and diagnosis in many hospitals. Howev...

Artificial intelligence to diagnose ischemic stroke and identify large vessel occlusions: a systematic review.

BACKGROUND AND PURPOSE: Acute stroke caused by large vessel occlusions (LVOs) requires emergent detection and treatment by endovascular thrombectomy. ...

A Dual-Modal Attention-Enhanced Deep Learning Network for Quantification of Parkinson's Disease Characteristics.

It is well known that most patients with Parkinson's disease (PD) have different degree of movement disorders, such as shuffling, festination and akin...

A Deep Learning Approach to Denoise Optical Coherence Tomography Images of the Optic Nerve Head.

Optical coherence tomography (OCT) has become an established clinical routine for the in vivo imaging of the optic nerve head (ONH) tissues, that is c...

Automated Detection of Parkinson's Disease Based on Multiple Types of Sustained Phonations Using Linear Discriminant Analysis and Genetically Optimized Neural Network.

OBJECTIVE: Parkinson's disease (PD) is a serious neurodegenerative disorder. It is reported that most of PD patients have voice impairments. But these...

Bag of Samplings for computer-assisted Parkinson's disease diagnosis based on Recurrent Neural Networks.

Parkinson's Disease (PD) is a clinical syndrome that affects millions of people worldwide. Although considered as a non-lethal disease, PD shortens th...

Applying density-based outlier identifications using multiple datasets for validation of stroke clinical outcomes.

INTRODUCTION: Clinicians commonly use the modified Rankin Scale (mRS) and the Barthel Index (BI) to measure clinical outcome after stroke. These are p...

A Multichannel Convolutional Neural Network Architecture for the Detection of the State of Mind Using Physiological Signals from Wearable Devices.

Detection of the state of mind has increasingly grown into a much favored study in recent years. After the advent of smart wearables in the market, ea...

Generalizability of machine learning for classification of schizophrenia based on resting-state functional MRI data.

Machine learning has increasingly been applied to classification of schizophrenia in neuroimaging research. However, direct replication studies and st...

Predicting post-stroke pneumonia using deep neural network approaches.

BACKGROUND AND PURPOSE: Pneumonia is a common complication after stroke, causing an increased length of hospital stay and death. Therefore, the timely...

Automatic Seizure Detection Based on S-Transform and Deep Convolutional Neural Network.

Automatic seizure detection is significant for the diagnosis of epilepsy and reducing the massive workload of reviewing continuous EEGs. In this work,...

Deep Learning Approaches Predict Glaucomatous Visual Field Damage from OCT Optic Nerve Head En Face Images and Retinal Nerve Fiber Layer Thickness Maps.

PURPOSE: To develop and evaluate a deep learning system for differentiating between eyes with and without glaucomatous visual field damage (GVFD) and ...

A strategy combining intrinsic time-scale decomposition and a feedforward neural network for automatic seizure detection.

UNLABELLED: Epilepsy is a common neurological disorder which can occur in people of all ages globally. For the clinical treatment of epileptic patient...

Using Artificial Intelligence to Manage Thrombosis Research, Diagnosis, and Clinical Management.

Thrombosis development in either arterial or venous system remains a major cause of death and disability worldwide. This poorly controlled in vivo clo...

Orthogonal convolutional neural networks for automatic sleep stage classification based on single-channel EEG.

BACKGROUND AND OBJECTIVE: In recent years, several automatic sleep stage classification methods based on convolutional neural networks (CNN) by learni...

Label Self-Advised Support Vector Machine (LSA-SVM)-Automated Classification of Foot Drop Rehabilitation Case Study.

Stroke represents a major health problem in our society. One of the effects of stroke is foot drop. Foot drop (FD) is a weakness that occurs in specif...

Multilevel Features for Sensor-Based Assessment of Motor Fluctuation in Parkinson's Disease Subjects.

Motor fluctuations are a frequent complication in patients with Parkinson's disease (PD) where the response to medication fluctuates between ON states...

Post-hoc modification of linear models: Combining machine learning with domain information to make solid inferences from noisy data.

Linear machine learning models "learn" a data transformation by being exposed to examples of input with the desired output, forming the basis for a va...