Neurology

Enlarged perivascular spaces in brain MRI: Automated quantification in four regions.

Enlarged perivascular spaces (PVS) are structural brain changes visible in MRI, are common in aging, and are considered a reflection of cerebral small vessel disease. As such, assessing the burden of PVS has promise as a brain imaging marker. Visual and manual scoring of PVS is a tedious and observer-dependent task. Automated methods would advance research into the etiology of PVS, could aid to as...

Using deep autoencoders to identify abnormal brain structural patterns in neuropsychiatric disorders: A large-scale multi-sample study.

Machine learning is becoming an increasingly popular approach for investigating spatially distributed and subtle neuroanatomical alterations in brain-based disorders. However, some machine learning models have been criticized for requiring a large number of cases in each experimental group, and for resembling a "black box" that provides little or no insight into the nature of the data. In this art...

Automated seizure prediction.

In the past two decades, significant advances have been made on automated electroencephalogram (EEG)-based diagnosis of epilepsy and seizure detection...

Multimodal MRI-based classification of migraine: using deep learning convolutional neural network.

BACKGROUND: Recently, deep learning technologies have rapidly expanded into medical image analysis, including both disease detection and classificatio...

A Robust AUC Maximization Framework With Simultaneous Outlier Detection and Feature Selection for Positive-Unlabeled Classification.

The positive-unlabeled (PU) classification is a common scenario in real-world applications such as healthcare, text classification, and bioinformatics...

Automatic segmentation of the spinal cord and intramedullary multiple sclerosis lesions with convolutional neural networks.

The spinal cord is frequently affected by atrophy and/or lesions in multiple sclerosis (MS) patients. Segmentation of the spinal cord and lesions from...

Automatic detection of oral and pharyngeal phases in swallowing using classification algorithms and multichannel EMG.

Swallowing is a complex process that involves sequential voluntary and involuntary muscle contractions. Malfunctioning of swallowing related muscles c...

Machine-learning based lipid mediator serum concentration patterns allow identification of multiple sclerosis patients with high accuracy.

Based on increasing evidence suggesting that MS pathology involves alterations in bioactive lipid metabolism, the present analysis was aimed at genera...

Prediction of spinal curve progression in Adolescent Idiopathic Scoliosis using Random Forest regression.

BACKGROUND: The progression of the spinal curve represents one of the major concerns in the assessment of Adolescent Idiopathic Scoliosis (AIS). The p...

Deep learning based low-cost high-accuracy diagnostic framework for dementia using comprehensive neuropsychological assessment profiles.

BACKGROUND: The conventional scores of the neuropsychological batteries are not fully optimized for diagnosing dementia despite their variety and abun...

Translational machine learning for psychiatric neuroimaging.

Despite its initial promise, neuroimaging has not been widely translated into clinical psychiatry to assist in the prediction of diagnoses, prognoses,...

Alzheimer's disease diagnosis based on multiple cluster dense convolutional networks.

Alzheimer's disease (AD) is an irreversible neurodegenerative disorder with progressive impairment of memory and cognitive functions. Structural magne...

Optimal cerebral perfusion pressure via transcranial Doppler in TBI: application of robotic technology.

Individualized cerebral perfusion pressure (CPP) targets may be derived via assessing the minimum of the parabolic relationship between an index of ce...

Effects of hyaluronidase on ropivacaine or bupivacaine regional anaesthesia of the canine pelvic limb.

OBJECTIVE: To determine the effect of hyaluronidase on time to onset and offset of anaesthesia in ropivacaine or bupivacaine femoral-ischiatic nerve b...

The application of artificial intelligence to understand the pathophysiological basis of psychogenic nonepileptic seizures.

Psychogenic nonepileptic seizures (PNES) are episodes of paroxysmal impairment associated with a range of motor, sensory, and mental manifestations, w...

Robotic Assessment of Motor, Sensory, and Cognitive Function in Acute Sport-Related Concussion and Recovery.

There is a need for better tools to objectively, reliably, and precisely assess neurological function after sport-related concussion (SRC). The aim of...

A review on microelectrode recording selection of features for machine learning in deep brain stimulation surgery for Parkinson's disease.

OBJECTIVE: This study seeks to systematically review the selection of features and algorithms for machine learning and automation in deep brain stimul...

The Combined Use of Transcranial Direct Current Stimulation and Robotic Therapy for the Upper Limb.

Neurologic disorders such as stroke and cerebral palsy are leading causes of long-term disability and can lead to severe incapacity and restriction of...

Dialogue Systems and Conversational Agents for Patients with Dementia: The Human-Robot Interaction.

This study aimed to identify and describe the fundamental characteristics of spoken dialogue systems, and their role in supporting human-robot interac...

Instance-Based Representation Using Multiple Kernel Learning for Predicting Conversion to Alzheimer Disease.

The early detection of Alzheimer's disease and quantification of its progression poses multiple difficulties for machine learning algorithms. Two of t...