Neurology

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

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Current trends and future perspectives of stroke management through integrating health care team and nanodrug delivery strategy.

Stroke is accounted as the second-most mortality and adult disability factor in worldwide, while cau...

Brain age predicted using graph convolutional neural network explains neurodevelopmental trajectory in preterm neonates.

OBJECTIVES: Dramatic brain morphological changes occur throughout the third trimester of gestation. ...

Machine learning and artificial intelligence in neuroscience: A primer for researchers.

Artificial intelligence (AI) is often used to describe the automation of complex tasks that we would...

Performance evaluation of metaheuristics-tuned recurrent neural networks for electroencephalography anomaly detection.

Electroencephalography (EEG) serves as a diagnostic technique for measuring brain waves and brain ac...

Anti-inflammatory and antioxidative actions of tacrolimus (FK506) on human microglial HMC3 cell line.

Microglial cells are indispensable for the normal development and functioning of neurons in the cent...

Utilizing deep learning via the 3D U-net neural network for the delineation of brain stroke lesions in MRI image.

The segmentation of acute stroke lesions plays a vital role in healthcare by assisting doctors in ma...

Space-CNN: a decision classification method based on EEG signals from different brain regions.

Decision-making plays a critical role in an individual's interpersonal interactions and cognitive pr...

Prediction of cerebral hemorrhagic transformation after thrombectomy using a deep learning of dual-energy CT.

OBJECTIVES: To develop and validate a deep learning model for predicting hemorrhagic transformation ...

In-depth quantification of bimanual coordination using the Kinarm exoskeleton robot in children with unilateral cerebral palsy.

BACKGROUND: Robots have been proposed as tools to measure bimanual coordination in children with uni...

Neurodynamic approaches for multi-agent distributed optimization.

This paper considers a class of multi-agent distributed convex optimization with a common set of con...

A narrative review of radiomics and deep learning advances in neuroblastoma: updates and challenges.

Neuroblastoma is an extremely heterogeneous tumor that commonly occurs in children. The diagnosis an...

Deep learning-based image analysis identifies a DAT-negative subpopulation of dopaminergic neurons in the lateral Substantia nigra.

Here we present a deep learning-based image analysis platform (DLAP), tailored to autonomously quant...

Miner Fatigue Detection from Electroencephalogram-Based Relative Power Spectral Topography Using Convolutional Neural Network.

Fatigue of miners is caused by intensive workloads, long working hours, and shift-work schedules. It...

Localization of early infarction on non-contrast CT images in acute ischemic stroke with deep learning approach.

Localization of early infarction on first-line Non-contrast computed tomogram (NCCT) guides prompt t...

Hybrid optimization assisted channel selection of EEG for deep learning model-based classification of motor imagery task.

OBJECTIVES: To design and develop an approach named HC + SMA-SSA scheme for classifying motor imager...

Illuminating the Neural Landscape of Pilot Mental States: A Convolutional Neural Network Approach with Shapley Additive Explanations Interpretability.

Predicting pilots' mental states is a critical challenge in aviation safety and performance, with el...

Accurate Monitoring of 24-h Real-World Movement Behavior in People with Cerebral Palsy Is Possible Using Multiple Wearable Sensors and Deep Learning.

Monitoring and quantifying movement behavior is crucial for improving the health of individuals with...

Gait training using a wearable robotic hip device for incomplete spinal cord injury: A preliminary study.

CONTEXT/OBJECTIVE: To explore changes in gait functions for patients with chronic spinal cord injury...

IMH-Net: a convolutional neural network for end-to-end EEG motor imagery classification.

As the main component of Brain-computer interface (BCI) technology, the classification algorithm bas...

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