Neurology

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

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A Novel Data Augmentation Approach Using Mask Encoding for Deep Learning-Based Asynchronous SSVEP-BCI.

Deep learning (DL)-based methods have been successfully employed as asynchronous classification algo...

A stroke prediction framework using explainable ensemble learning.

The death of brain cells occurs when blood flow to a particular area of the brain is abruptly cut of...

Retinal OCT biomarkers and their association with cognitive function-clinical and AI approaches.

Retinal optical coherence tomography (OCT) biomarkers have the potential to serve as early, noninvas...

Automatic generation of conclusions from neuroradiology MRI reports through natural language processing.

PURPOSE: The conclusion section of a radiology report is crucial for summarizing the primary radiolo...

A multimodal deep learning approach for the prediction of cognitive decline and its effectiveness in clinical trials for Alzheimer's disease.

Alzheimer's disease is one of the most important health-care challenges in the world. For decades, n...

Magnetic soft microfiberbots for robotic embolization.

Cerebral aneurysms and brain tumors are leading life-threatening diseases worldwide. By deliberately...

Effect of robotic gait training on muscle and bone characteristics in spinal cord transected rats.

Osteoporosis and loss of muscle mass are secondary issues with spinal cord injury. Robotic gait trai...

Deep learning and predictive modelling for generating normalised muscle function parameters from signal images of mandibular electromyography.

Challenges arise in accessing archived signal outputs due to proprietary software limitations. There...

Predictive deep learning models for cognitive risk using accessible data.

The early detection of mild cognitive impairment (MCI) is crucial to preventing the progression of d...

Ranking and filtering of neuropathology features in the machine learning evaluation of dementia studies.

Early diagnosis of dementia diseases, such as Alzheimer's disease, is difficult because of the time ...

Intelligent decision support systems for dementia care: A scoping review.

In the context of dementia care, Artificial Intelligence (AI) powered clinical decision support syst...

Greater accuracy of radiomics compared to deep learning to discriminate normal subjects from patients with dementia: a whole brain 18FDG PET analysis.

METHODS: 18F-FDG brain PET and clinical score were collected in 85 patients with dementia and 125 he...

Stretchable and neuromorphic transistors for pain perception and sensitization emulation.

Pain perception nociceptors (PPN), an important type of sensory neuron, are capable of sending out a...

Timing matters for accurate identification of the epileptogenic zone.

OBJECTIVE: Interictal biomarkers of the epileptogenic zone (EZ) and their use in machine learning mo...

Classification of self-limited epilepsy with centrotemporal spikes by classical machine learning and deep learning based on electroencephalogram data.

Electroencephalogram (EEG) has been widely utilized as a valuable assessment tool for diagnosing epi...

Artificial intelligence in neurology: opportunities, challenges, and policy implications.

Neurological conditions are the leading cause of disability and mortality combined, demanding innova...

Frame-based versus robot-assisted stereo-electro-encephalography for drug-resistant epilepsy.

BACKGROUND: Stereoelectroencephalography (SEEG) is an effective presurgical invasive evaluation for ...

Retinal imaging and Alzheimer's disease: a future powered by Artificial Intelligence.

Alzheimer's disease (AD) is a neurodegenerative condition that primarily affects brain tissue. Becau...

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