Neurology

Comparison of morphometric parameters in prediction of hydrocephalus using random forests.

Ventricles of the human brain enlarge with aging, neurodegenerative diseases, intrinsic, and extrinsic pathologies. The morphometric examination of neuroimages is an effective approach to assess structural changes occurring due to diseases such as hydrocephalus. In this study, we explored the effectiveness of commonly used morphological parameters in hydrocephalus diagnosis. For this purpose, the ...

Use of deep learning to detect personalized spatial-frequency abnormalities in EEGs of children with ADHD.

OBJECTIVE: Attention-deficit/hyperactivity disorder (ADHD) is one of the most prevalent neurobehavioral disorders. Studies have tried to find the neural correlations of ADHD with electroencephalography (EEG). Due to the heterogeneity in the ADHD population, a multivariate EEG profile is useful, and the detection of a personalized abnormality in EEG is urgently needed. Deep learning algorithms, esp...

Integrating machining learning and multimodal neuroimaging to detect schizophrenia at the level of the individual.

Schizophrenia is a severe psychiatric disorder associated with both structural and functional brain abnormalities. In the past few years, there has be...

Statistical modeling and knowledge-based segmentation of cerebral artery based on TOF-MRA and MR-T1.

BACKGROUND AND OBJECTIVE: For cerebrovascular segmentation from time-of-flight (TOF) magnetic resonance angiography (MRA), the focused issues are segm...

Semi-supervised Training Data Selection Improves Seizure Forecasting in Canines with Epilepsy.

OBJECTIVE: Conventional selection of pre-ictal EEG epochs for seizure prediction algorithm training data typically assumes a continuous pre-ictal brai...

Volumetry of Mesiotemporal Structures Reflects Serostatus in Patients with Limbic Encephalitis.

BACKGROUND AND PURPOSE: Limbic encephalitis is an autoimmune disease. A variety of autoantibodies have been associated with different subtypes of limb...

Predicting O-Water PET cerebral blood flow maps from multi-contrast MRI using a deep convolutional neural network with evaluation of training cohort bias.

To improve the quality of MRI-based cerebral blood flow (CBF) measurements, a deep convolutional neural network (dCNN) was trained to combine single- ...

Subject-Independent Brain-Computer Interfaces Based on Deep Convolutional Neural Networks.

For a brain-computer interface (BCI) system, a calibration procedure is required for each individual user before he/she can use the BCI. This procedur...

Human Versus Machine: Comparing a Deep Learning Algorithm to Human Gradings for Detecting Glaucoma on Fundus Photographs.

PURPOSE: To compare the diagnostic performance of human gradings vs predictions provided by a machine-to-machine (M2M) deep learning (DL) algorithm tr...

An artificial intelligence-based deep learning algorithm for the diagnosis of diabetic neuropathy using corneal confocal microscopy: a development and validation study.

AIMS/HYPOTHESIS: Corneal confocal microscopy is a rapid non-invasive ophthalmic imaging technique that identifies peripheral and central neurodegenera...

Molecular basis of degenerative spinal disorders from a proteomic perspective (Review).

Intervertebral disc degeneration (IDD) and ligamentum flavum hypertrophy (LFH) are major causes of degenerative spinal disorders. Comparative and prot...

A machine learning algorithm successfully screens for Parkinson's in web users.

OBJECTIVE: To develop, apply, and evaluate, a novel web-based classifier for screening for Parkinson disease among a large cohort of search engine use...

Automated Parkinson's disease recognition based on statistical pooling method using acoustic features.

Parkinson's disease is one of the mostly seen neurological disease. It affects to nervous system and hinders people's vital activities. The majority o...

Detection of mesial temporal lobe epileptiform discharges on intracranial electrodes using deep learning.

OBJECTIVE: Develop a high-performing algorithm to detect mesial temporal lobe (mTL) epileptiform discharges on intracranial electrode recordings.

Optimized artificial neural network based performance analysis of wheelchair movement for ALS patients.

Individuals with neurodegenerative attacks loose the entire motor neuron movements. These conditions affect the individual actions like walking, speak...

Signal identification system for developing rehabilitative device using deep learning algorithms.

Paralyzed patients were increasing day by day. Some of the neurodegenerative diseases like amyotrophic lateral sclerosis, Brainstem Leison, Stupor and...

Forecasting Retinal Nerve Fiber Layer Thickness from Multimodal Temporal Data Incorporating OCT Volumes.

PURPOSE: The purpose of this study was to develop a machine learning model to forecast future circumpapillary retinal nerve fiber layer (cpRNFL) thick...

Safety and immediate effects of Hybrid Assistive Limb in children with cerebral palsy: A pilot study.

PURPOSE: Early intervention is effective for developing motor ability and preventing contractures and deformities in patients with cerebral palsy (CP)...

Seizures in Human Immunodeficiency Virus-Associated Cryptococcal Meningitis: Predictors and Outcomes.

BACKGROUND: Seizures commonly occur in patients with cryptococcal meningitis, yet risk factors and outcomes related to seizures are not well described...

Quantitative susceptibility mapping based hybrid feature extraction for diagnosis of Parkinson's disease.

Parkinson's disease is the second most common neurodegenerative disease in the elderly after Alzheimer's disease. The aetiology and pathogenesis of Pa...