Neurology

Time-Frequency Decomposition of Scalp Electroencephalograms Improves Deep Learning-Based Epilepsy Diagnosis.

Epilepsy diagnosis based on Interictal Epileptiform Discharges (IEDs) in scalp electroencephalograms (EEGs) is laborious and often subjective. Therefore, it is necessary to build an effective IED detector and an automatic method to classify IED-free versus IED EEGs. In this study, we evaluate features that may provide reliable IED detection and EEG classification. Specifically, we investigate the ...

Reliable and valid robot-assisted assessments of hand proprioceptive, motor and sensorimotor impairments after stroke.

BACKGROUND: Neurological injuries such as stroke often differentially impair hand motor and somatosensory function, as well as the interplay between the two, which leads to limitations in performing activities of daily living. However, it is challenging to identify which specific aspects of sensorimotor function are impaired based on conventional clinical assessments that are often insensitive and...

Fast deep neural correspondence for tracking and identifying neurons in using semi-synthetic training.

We present an automated method to track and identify neurons in , called 'fast Deep Neural Correspondence' or fDNC, based on the transformer network a...

Use of Deep-Learning Genomics to Discriminate Healthy Individuals from Those with Alzheimer's Disease or Mild Cognitive Impairment.

OBJECTIVES: Alzheimer's disease (AD) is the most prevalent neurodegenerative disorder and the most common form of dementia in the elderly. Certain gen...

Deep Learning on MRI Images for Diagnosis of Lung Cancer Spinal Bone Metastasis.

This paper aimed to explore the adoption of deep learning algorithms in lung cancer spinal bone metastasis diagnosis. Comprehensive analysis was carri...

Ocular blood flow as it relates to race and disease on glaucoma.

Glaucoma is a multifactorial progressive and degenerative optic neuropathy representing one of the world's leading cause of irreversible blindness. Cu...

Using deep learning to identify the recurrent laryngeal nerve during thyroidectomy.

Surgeons must visually distinguish soft-tissues, such as nerves, from surrounding anatomy to prevent complications and optimize patient outcomes. An a...

Exploration of physiological sensors, features, and machine learning models for pain intensity estimation.

In current clinical settings, typically pain is measured by a patient's self-reported information. This subjective pain assessment results in suboptim...

Automatic detection of papilledema through fundus retinal images using deep learning.

Papilledema is a syndrome of the retina in which retinal optic nerve is inflated by elevation of intracranial pressure. The papilledema abnormalities ...

Clinical effects of assisted robotic gait training in walking distance, speed, and functionality are maintained over the long term in individuals with cerebral palsy: a systematic review and meta-analysis.

PURPOSE: To identify the short-term effects of robotic-assisted gait training (RAGT) on walking distance, gait speed and functionality of cerebral pal...

Emotion-Driven Analysis and Control of Human-Robot Interactions in Collaborative Applications.

The utilization of robotic systems has been increasing in the last decade. This increase has been derived by the evolvement in the computational capab...

Time-Frequency Representation and Convolutional Neural Network-Based Emotion Recognition.

Emotions composed of cognizant logical reactions toward various situations. Such mental responses stem from physiological, cognitive, and behavioral c...

The Application of Convolutional Neural Network Model in Diagnosis and Nursing of MR Imaging in Alzheimer's Disease.

The disease Alzheimer is an irrepressible neurologicalbrain disorder. Earlier detection and proper treatment of Alzheimer's disease can help for brain...

Morphological analysis of Kambin's triangle using 3D CT/MRI fusion imaging of lumbar nerve root created automatically with artificial intelligence.

PURPOSE: We developed a software program that automatically extracts a three-dimensional (3D) lumbar nerve root image from magnetic resonance imaging ...

Targeted muscle effort distribution with exercise robots: Trajectory and resistance effects.

The objective of this work is to relate muscle effort distributions to the trajectory and resistance settings of a robotic exercise and rehabilitation...

AIOSA: An approach to the automatic identification of obstructive sleep apnea events based on deep learning.

Obstructive Sleep Apnea Syndrome (OSAS) is the most common sleep-related breathing disorder. It is caused by an increased upper airway resistance duri...

Artificial Intelligence Analysis of EEG Amplitude in Intensive Heart Care.

This article first studied the morphological characteristics of the EEG for intensive cardiac care; that is, based on the analysis of the mechanism of...

Machine learning compensates fold-change method and highlights oxidative phosphorylation in the brain transcriptome of Alzheimer's disease.

Alzheimer's disease (AD) is a neurodegenerative disorder causing 70% of dementia cases. However, the mechanism of disease development is still elusive...

Neuromate robot-assisted ventricular catheter insertion.

BACKGROUND AND IMPORTANCE: Insertion of ventricular catheters into small ventricles may require image guidance. Several options exist, including ultra...

On the Role of Arkypallidal and Prototypical Neurons for Phase Transitions in the External Pallidum.

The external pallidum (globus pallidus pars externa [GPe]) plays a central role for basal ganglia functions and dynamics and, consequently, has been i...