Neurology

Computer-Aided Diagnosis for Determining Sagittal Spinal Curvatures Using Deep Learning and Radiography.

Analyzing spinal curvatures manually is time-consuming and tedious for clinicians, and intra-observer and inter-observer variability can affect manual measurements. In this study, we developed and evaluated the performance of an automated deep learning-based computer-aided diagnosis (CAD) tool for measuring the sagittal alignment of the spine from X-ray images. The CAD system proposed here perform...

Towards accurate facial nerve segmentation with decoupling optimization.

Robotic cochlear implantation is an effective way to restore the hearing of hearing-impaired patients, and facial nerve recognition is the key to the operation. However, accurate facial nerve segmentation is a challenging task, mainly for two key issues: (1) the facial nerve area is very small in image, and there are many similar areas; (2) low contrast of the border between the facial nerve and t...

Deep Multi-Scale Fusion of Convolutional Neural Networks for EMG-Based Movement Estimation.

EMG-based motion estimation is required for applications such as myoelectric control, where the simultaneous estimation of kinematic information, name...

Robot-assisted training using hybrid assistive limb ameliorates gait ability in patients with amyotrophic lateral sclerosis.

OBJECTIVE: The Hybrid Assistive Limb (HAL; CYBERDYNE, Inc., Japan) is a wearable robot device that provides effective gait assistance according to vol...

Generalized Fast Discharges Along the Genetic Generalized Epilepsy Spectrum: Clinical and Prognostic Significance.

OBJECTIVE: To investigate the electroclinical characteristics and the prognostic impact of generalized fast discharges in a large cohort of genetic ge...

Neuroprotective and Therapeutic Effects of Tocovid and Twendee-X on Aβ Oligomer-Induced Damage in the SH-SY5Y Cell Line.

BACKGROUND: Alzheimer's disease (AD) is the most frequent cause of dementia among the elderly. The accumulation of amyloid beta (Aβ) and its downstrea...

Automatic sleep stage classification: A light and efficient deep neural network model based on time, frequency and fractional Fourier transform domain features.

This work proposed a novel method for automatic sleep stage classification based on the time, frequency, and fractional Fourier transform (FRFT) domai...

Corticomuscular integrated representation of voluntary motor effort in robotic control for wrist-hand rehabilitation after stroke.

The central-to-peripheral voluntary motor effort (VME) in the affected limb is a dominant force for driving the functional neuroplasticity on motor re...

Identification of Pharmacophoric Fragments of DYRK1A Inhibitors Using Machine Learning Classification Models.

Dual-specific tyrosine phosphorylation regulated kinase 1 (DYRK1A) has been regarded as a potential therapeutic target of neurodegenerative diseases, ...

Genetic Programming-Based Feature Selection for Emotion Classification Using EEG Signal.

The COVID-19 has resulted in one of the world's most significant worldwide lock-downs, affecting human mental health. Therefore, emotion recognition i...

Accurate identification of EEG recordings with interictal epileptiform discharges using a hybrid approach: Artificial intelligence supervised by human experts.

OBJECTIVE: To evaluate the diagnostic performance of artificial intelligence (AI)-based algorithms for identifying the presence of interictal epilepti...

A Deep Shared Multi-Scale Inception Network Enables Accurate Neonatal Quiet Sleep Detection With Limited EEG Channels.

In this paper, we introduce a new variation of the Convolutional Neural Network Inception block, called Sinc, for sleep stage classification in premat...

Postsurgical complications after robot-assisted transaxillary thyroidectomy: critical analysis of a large cohort of European patients.

In the last decade, robot-assisted trans-axillary thyroidectomy has spread rapidly and has been proven to be a safe and effective procedure. However, ...

Two-dimensional CNN-based distinction of human emotions from EEG channels selected by multi-objective evolutionary algorithm.

In this study we explore how different levels of emotional intensity (Arousal) and pleasantness (Valence) are reflected in electroencephalographic (EE...

Cognitive Impairment of Patient With Neurological Cerebrovascular Disease Using the Artificial Intelligence Technology Guided by MRI.

This study was to explore the application of MRI based on artificial intelligence technology combined with neuropsychological assessment to the cognit...

Improving Alzheimer's Disease Detection for Speech Based on Feature Purification Network.

Alzheimer's disease (AD) is a neurodegenerative disease involving the decline of cognitive ability with illness progresses. At present, the diagnosis ...

An attention-based hybrid deep learning framework integrating brain connectivity and activity of resting-state functional MRI data.

Functional magnetic resonance imaging (fMRI) as a promising tool to investigate psychotic disorders can be decomposed into useful imaging features suc...

Deep learning identify retinal nerve fibre and choroid layers as markers of age-related macular degeneration in the classification of macular spectral-domain optical coherence tomography volumes.

SDnDTI: Self-supervised deep learning-based denoising for diffusion tensor MRI.

Diffusion tensor magnetic resonance imaging (DTI) is a widely adopted neuroimaging method for the in vivo mapping of brain tissue microstructure and w...

Brain Decoding Using fMRI Images for Multiple Subjects through Deep Learning.

Substantial information related to human cerebral conditions can be decoded through various noninvasive evaluating techniques like fMRI. Exploration o...