Neurology

Latest AI and machine learning research in neurology for healthcare professionals.

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A Co-Designed Neuromorphic Chip With Compact (17.9K F) and Weak Neuron Number-Dependent Neuron/Synapse Modules.

Many efforts have been made to improve the neuron integration efficiency on neuromorphic chips, such...

Leveraging Multiple Distinct EEG Training Sessions for Improvement of Spectral-Based Biometric Verification Results.

Most studies on EEG-based biometry recognition report results based on signal databases, with a limi...

CNTSeg: A multimodal deep-learning-based network for cranial nerves tract segmentation.

The segmentation of cranial nerves (CNs) tracts based on diffusion magnetic resonance imaging (dMRI)...

Classification of Motor Imagery EEG Signals Based on Data Augmentation and Convolutional Neural Networks.

In brain-computer interface (BCI) systems, motor imagery electroencephalography (MI-EEG) signals are...

NeuroPpred-SVM: A New Model for Predicting Neuropeptides Based on Embeddings of BERT.

Neuropeptides play pivotal roles in different physiological processes and are related to different k...

Nurses' perception towards care robots and their work experience with socially assistive technology during COVID-19: A qualitative study.

This study aimed to explore nurses' perceptions towards care robots and their work experiences in ca...

Real-Time Automated Segmentation of Median Nerve in Dynamic Ultrasonography Using Deep Learning.

OBJECTIVE: The morphological dynamics of the median nerve across the level extracted from dynamic ul...

Interpretable machine learning for dementia: A systematic review.

INTRODUCTION: Machine learning research into automated dementia diagnosis is becoming increasingly p...

The role of Artificial intelligence in the assessment of the spine and spinal cord.

Artificial intelligence (AI) application development is underway in all areas of radiology where man...

Interaction with Industrial Digital Twin Using Neuro-Symbolic Reasoning.

Digital twins have revolutionized manufacturing and maintenance, allowing us to interact with virtua...

Convolution Neural Networks and Self-Attention Learners for Alzheimer Dementia Diagnosis from Brain MRI.

Alzheimer's disease (AD) is the most common form of dementia. Computer-aided diagnosis (CAD) can hel...

A Spatiotemporal Graph Attention Network Based on Synchronization for Epileptic Seizure Prediction.

Accurate early prediction of epileptic seizures can provide timely treatment for patients. Previous ...

Cognitive Depression Detection Cyber-Medical System Based on EEG Analysis and Deep Learning Approaches.

Long-term depression and negative emotional cycles affect life quality and work productivity. Howeve...

Mutual Information-Driven Subject-Invariant and Class-Relevant Deep Representation Learning in BCI.

In recent years, deep learning-based feature representation methods have shown a promising impact on...

Deep Learning System Outperforms Clinicians in Identifying Optic Disc Abnormalities.

BACKGROUND: The examination of the optic nerve head (optic disc) is mandatory in patients with heada...

EEG emotion recognition using improved graph neural network with channel selection.

BACKGROUND AND OBJECTIVE: Emotion classification tasks based on electroencephalography (EEG) are an ...

SynthSR: A public AI tool to turn heterogeneous clinical brain scans into high-resolution T1-weighted images for 3D morphometry.

Every year, millions of brain magnetic resonance imaging (MRI) scans are acquired in hospitals acros...

Restoring arm function with a soft robotic wearable for individuals with amyotrophic lateral sclerosis.

Despite promising results in the rehabilitation field, it remains unclear whether upper limb robotic...

Intraoperative Navigation and Robotics in Pediatric Spinal Deformity.

Current technologies for image guidance navigation and robotic assistance with spinal surgery are im...

An Unsupervised Learning-Based Regional Deformable Model for Automated Multi-Organ Contour Propagation.

The aim of this study is to evaluate a regional deformable model based on a deep unsupervised learni...

The effect of robot-assisted gait training frequency on walking, functional recovery, and quality of life in patients with stroke.

AIM: This study aims to investigate the effects of robot-assisted gait training (RAGT) frequency on ...

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