Neurology

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

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Neuropathologist-level integrated classification of adult-type diffuse gliomas using deep learning from whole-slide pathological images.

Current diagnosis of glioma types requires combining both histological features and molecular charac...

A low-power vertical dual-gate neurotransistor with short-term memory for high energy-efficient neuromorphic computing.

Neuromorphic computing aims to emulate the computing processes of the brain by replicating the funct...

Evaluation of a novel real-time adaptive assist-as-needed controller for robot-assisted upper extremity rehabilitation following stroke.

Rehabilitation therapy plays an essential role in assisting people with stroke regain arm function. ...

A preliminary exploration into top-down and bottom-up deep-learning approaches to localising neuro-interventional point targets in volumetric MRI.

PURPOSE: Point localisation is a critical aspect of many interventional planning procedures, specifi...

Speech and language processing with deep learning for dementia diagnosis: A systematic review.

Dementia is a progressive neurodegenerative disease that burdens the person living with the disease,...

Investigation of the mechanism of action of deep brain stimulation for the treatment of Parkinson's disease.

Parkinson's disease (PD) is a severe, progressive, neurological disorder. PD is not a single disease...

Circular-SWAT for deep learning based diagnostic classification of Alzheimer's disease: application to metabolome data.

BACKGROUND: Deep learning has shown potential in various scientific domains but faces challenges whe...

Bayesian learning from multi-way EEG feedback for robot navigation and target identification.

Many brain-computer interfaces require a high mental workload. Recent research has shown that this c...

Quantitative noninvasive measurement of cerebrospinal fluid flow in shunted hydrocephalus.

OBJECTIVE: Standard MRI protocols lack a quantitative sequence that can be used to evaluate shunt-tr...

The effect of sequential combination of mirror therapy and robot-assisted therapy on motor function, daily function, and self-efficacy after stroke.

Robot-assisted therapy and mirror therapy are both effective in promoting upper limb function after ...

Quantifying innervation facilitated by deep learning in wound healing.

The peripheral nerves (PNs) innervate the dermis and epidermis, and are suggested to play an importa...

Ethical Considerations in Neuroprognostication Following Acute Brain Injury.

Neuroprognostication following acute brain injury (ABI) is a complex process that involves integrati...

TFEB/LAMP2 contributes to PM-induced autophagy-lysosome dysfunction and alpha-synuclein dysregulation in astrocytes.

Atmospheric particulate matter (PM) exacerbates the risk factor for Alzheimer's and Parkinson's dise...

A deep learning-based telemonitoring application to automatically assess oral diadochokinesis in patients with bulbar amyotrophic lateral sclerosis.

BACKGROUND AND OBJECTIVES: Timely identification of dysarthria progression in patients with bulbar-o...

Effects of MRI scanner manufacturers in classification tasks with deep learning models.

Deep learning has become a leading subset of machine learning and has been successfully employed in ...

EEG-Based Cross-Subject Driver Drowsiness Recognition With an Interpretable Convolutional Neural Network.

In the context of electroencephalogram (EEG)-based driver drowsiness recognition, it is still challe...

Quantitative Electroencephalography Objectivity and Reliability in the Diagnosis and Management of Traumatic Brain Injury: A Systematic Review.

Persons with a history of traumatic brain injury (TBI) may exhibit short- and long-term cognitive d...

Differentiating spinal pathologies by deep learning approach.

BACKGROUND CONTEXT: Spinal pathologies are diverse in nature and, excluding trauma and degenerative ...

Spectral analysis and Bi-LSTM deep network-based approach in detection of mild cognitive impairment from electroencephalography signals.

Mild cognitive impairment (MCI) is a neuropsychological syndrome that is characterized by cognitive ...

Risk of data leakage in estimating the diagnostic performance of a deep-learning-based computer-aided system for psychiatric disorders.

Deep-learning approaches with data augmentation have been widely used when developing neuroimaging-b...

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