Neurology

Comparison of Supervised Machine Learning Algorithms for Classifying of Home Discharge Possibility in Convalescent Stroke Patients: A Secondary Analysis.

OBJECTIVES: Classifying the possibility of home discharge is important during stroke rehabilitation to support decision-making. There have been several studies on supervised machine learning algorithms, but only a few have compared the performance of different algorithms based on the same dataset for the classification of home discharge possibility. Therefore, we aimed to evaluate five supervised ...

Robot-assisted gait training in individuals with spinal cord injury: A systematic review for the clinical effectiveness of Lokomat.

BACKGROUND: Spinal cord injury (SCI) is a critical medical condition that causes numerous impairments leading to accompanying disability. Robotic-assisted gait training (RAGT) offers many advantages, including the capability to increase the intensity and total duration of training while maintaining a physiological gait pattern. The effects of the RAGT 'Lokomat' on various impairments following SCI...

Graph-theory based degree centrality combined with machine learning algorithms can predict response to treatment with antiepileptic medications in children with epilepsy.

BACKGROUND AND PURPOSE: The purpose of the current study is to detect changes of graph-theory-based degree centrality (DC) and their relationship with...

Magnetic Resonance Imaging Features of Cerebral Infarction in Critical Patients Based on Convolutional Neural Network.

The clinical application of the artificial intelligence-assisted system in imaging was investigated by analyzing the magnetic resonance imaging (MRI) ...

Diagnosis and Treatment Effect of Convolutional Neural Network-Based Magnetic Resonance Image Features on Severe Stroke and Mental State.

The purpose of this paper is to explore the impact of magnetic resonance imaging (MRI) image features based on convolutional neural network (CNN) algo...

Deep Learning with Neuroimaging and Genomics in Alzheimer's Disease.

A growing body of evidence currently proposes that deep learning approaches can serve as an essential cornerstone for the diagnosis and prediction of ...

Automated detection and segmentation of sclerotic spinal lesions on body CTs using a deep convolutional neural network.

PURPOSE: To develop a deep convolutional neural network capable of detecting spinal sclerotic metastases on body CTs.

Reversal of thoracic sympathectomy through robot-assisted microsurgical sympathetic trunk reconstruction with sural nerve graft and additional end-to-side coaptation of the intercostal nerves: A case report.

Thoracic sympathectomy to treat palmar hyperhidrosis (PH) has widely been performed. Many patients regret the surgery due to compensatory hyperhidrosi...

Predicting subclinical psychotic-like experiences on a continuum using machine learning.

Previous studies applying machine learning methods to psychosis have primarily been concerned with the binary classification of chronic schizophrenia ...

Automatic multiclass intramedullary spinal cord tumor segmentation on MRI with deep learning.

Spinal cord tumors lead to neurological morbidity and mortality. Being able to obtain morphometric quantification (size, location, growth rate) of the...

A deep learning framework with multi-perspective fusion for interictal epileptiform discharges detection in scalp electroencephalogram.

Interictal epileptiform discharges (IEDs) are an important and widely accepted biomarker used in the diagnosis of epilepsy based on scalp electroencep...

Automated Annotation of Epileptiform Burden and Its Association with Outcomes.

OBJECTIVE: This study was undertaken to determine the dose-response relation between epileptiform activity burden and outcomes in acutely ill patients...

End-to-end learnable EEG channel selection for deep neural networks with Gumbel-softmax.

To develop an efficient, embedded electroencephalogram (EEG) channel selection approach for deep neural networks, allowing us to match the channel sel...

Emerging role of artificial intelligence in stroke imaging.

: The recognition and therapy of patients with stroke is becoming progressively intricate as additional treatment choices become accessible and new as...

Value of Rehabilitation Training for Children with Cerebral Palsy Diagnosed and Analyzed by Computed Tomography Imaging Information Features under Deep Learning.

To analyze the brain CT imaging data of children with cerebral palsy (CP), deep learning-based electronic computed tomography (CT) imaging information...

Identification and Diagnosis of Cerebral Stroke through Deep Convolutional Neural Network-Based Multimodal MRI Images.

This study aimed to explore the application value of multimodal magnetic resonance imaging (MRI) images based on the deep convolutional neural network...

Recent trends in artificial intelligence-driven identification and development of anti-neurodegenerative therapeutic agents.

Neurological disorders affect various aspects of life. Finding drugs for the central nervous system is a very challenging and complex task due to the ...

Deep Learning of Explainable EEG Patterns as Dynamic Spatiotemporal Clusters and Rules in a Brain-Inspired Spiking Neural Network.

The paper proposes a new method for deep learning and knowledge discovery in a brain-inspired Spiking Neural Networks (SNN) architecture that enhances...

A deep learning algorithm for sleep stage scoring in mice based on a multimodal network with fine-tuning technique.

Sleep stage scoring is important to determine sleep structure in preclinical and clinical research. The aim of this study was to develop an automatic ...

Further Consideration of Lymphadenectomy Along the Left Recurrent Laryngeal Nerve During Robot-Assisted Minimally Invasive Esophagectomy.