Neurology

3-Dimensional Immunostaining and Automated Deep-Learning Based Analysis of Nerve Degeneration.

Multiple sclerosis (MS) is an autoimmune and neurodegenerative disease driven by inflammation and demyelination in the brain, spinal cord, and optic nerve. Optic neuritis, characterized by inflammation and demyelination of the optic nerve, is a symptom in many patients with MS. The optic nerve is the highway for visual information transmitted from the retina to the brain. It contains axons from th...

Automatic seizure detection by convolutional neural networks with computational complexity analysis.

BACKGROUND AND OBJECTIVES: Nowadays, an automated computer-aided diagnosis (CAD) is an approach that plays an important role in the detection of health issues. The main advantages should be in early diagnosis, including high accuracy and low computational complexity without loss of the model performance. One of these systems type is concerned with Electroencephalogram (EEG) signals and seizure det...

Brain-Computer Interface-Controlled Exoskeletons in Clinical Neurorehabilitation: Ready or Not?

The development of brain-computer interface-controlled exoskeletons promises new treatment strategies for neurorehabilitation after stroke or spinal c...

Robot-assisted versus navigation-assisted screw placement in spinal vertebrae.

PURPOSE: Both robots and navigation are effective strategies for optimizing screw placement, as compared to freehand placement. However, few studies h...

Sleep Staging Framework with Physiologically Harmonized Sub-Networks.

Sleep screening is an important tool for both healthcare and neuroscientific research. Automatic sleep scoring is an alternative to the time-consuming...

Interpretable brain disease classification and relevance-guided deep learning.

Deep neural networks are increasingly used for neurological disease classification by MRI, but the networks' decisions are not easily interpretable by...

Deep learning-regularized, single-step quantitative susceptibility mapping quantification.

The purpose of the current study was to develop deep learning-regularized, single-step quantitative susceptibility mapping (QSM) quantification, direc...

Staging study of single-channel sleep EEG signals based on data augmentation.

INTRODUCTION: Accurate sleep staging is an essential basis for sleep quality assessment and plays an important role in sleep quality research. However...

Dialogue agents for artificial intelligence-based conversational systems for cognitively disabled: a systematic review.

PURPOSE: We present a systematic literature review of dialogue agents for Artificial Intelligence (AI) and agent-based conversational systems dealing ...

Computed Tomography of the Spine : Systematic Review on Acquisition and Reconstruction Techniques to Reduce Radiation Dose.

The introduction of the first whole-body CT scanner in 1974 marked the beginning of cross-sectional spine imaging. In the last decades, the technologi...

"Cephalgia" or "migraine"? Solving the headache of assessing clinical reasoning using natural language processing.

In this op-ed, we discuss the advantages of leveraging natural language processing (NLP) in the assessment of clinical reasoning. Clinical reasoning i...

Identifying an Optimal Neuroinflammation Treatment Using a Nanoligomer Discovery Engine.

Acute activation of innate immune response in the brain, or neuroinflammation, protects this vital organ from a range of external pathogens and promot...

Real-time noise cancellation with deep learning.

Biological measurements are often contaminated with large amounts of non-stationary noise which require effective noise reduction techniques. We prese...

Keypoints for nerve preservation during total mesorectal excision demonstrated using the robotic approach: learning resource for surgeons-A video vignette.

Deep Learning-Assisted Detection of Glaucoma Progression in Spectral-Domain OCT.

PURPOSE: To develop and validate a deep learning (DL) model for detection of glaucoma progression using spectral-domain (SD)-OCT measurements of retin...

A Novel Fixed-Time Converging Neurodynamic Approach to Mixed Variational Inequalities and Applications.

This article proposes a novel fixed-time converging forward-backward-forward neurodynamic network (FXFNN) to deal with mixed variational inequalities ...

NeuroGrasp: Real-Time EEG Classification of High-Level Motor Imagery Tasks Using a Dual-Stage Deep Learning Framework.

Brain-computer interfaces (BCIs) have been widely employed to identify and estimate a user's intention to trigger a robotic device by decoding motor i...

An Ensemble of Deep Learning Enabled Brain Stroke Classification Model in Magnetic Resonance Images.

Brain stroke is a major cause of global death and it necessitates earlier identification process to reduce the mortality rate. Magnetic resonance imag...

Generative adversarial network constrained multiple loss autoencoder: A deep learning-based individual atrophy detection for Alzheimer's disease and mild cognitive impairment.

Exploring individual brain atrophy patterns is of great value in precision medicine for Alzheimer's disease (AD) and mild cognitive impairment (MCI). ...

Detection of K-complexes in EEG waveform images using faster R-CNN and deep transfer learning.

BACKGROUND: The electroencephalography (EEG) signal carries important information about the electrical activity of the brain, which may reveal many pa...