Neurology

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

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Using deep learning for an automatic detection and classification of the vascular bifurcations along the Circle of Willis.

Most of the intracranial aneurysms (ICA) occur on a specific portion of the cerebral vascular tree n...

Optimizing detection and deep learning-based classification of pathological high-frequency oscillations in epilepsy.

OBJECTIVE: This study aimed to explore sensitive detection methods for pathological high-frequency o...

MISPEL: A supervised deep learning harmonization method for multi-scanner neuroimaging data.

Large-scale data obtained from aggregation of already collected multi-site neuroimaging datasets has...

Decoding movement kinematics from EEG using an interpretable convolutional neural network.

Continuous decoding of hand kinematics has been recently explored for the intuitive control of elect...

A novel method for modeling effective connections between brain regions based on EEG signals and graph neural networks for motor imagery detection.

Classified as biomedical signal processing, cerebral signal processing plays a key role in human-com...

Distribution Patterns of Subgroups of Inhibitory Neurons Divided by Calbindin 1.

The inhibitory neurons in the brain play an essential role in neural network firing patterns by rele...

Self-Attentive Channel-Connectivity Capsule Network for EEG-Based Driving Fatigue Detection.

Deep neural networks have recently been successfully extended to EEG-based driving fatigue detection...

Developing and deploying deep learning models in brain magnetic resonance imaging: A review.

Magnetic resonance imaging (MRI) of the brain has benefited from deep learning (DL) to alleviate the...

Improving the Classification Performance of Dendrite Morphological Neurons.

Dendrite morphological neurons (DMNs) are neural models for pattern classification, where dendrites ...

Deep-learning detection of mild cognitive impairment from sleep electroencephalography for patients with Parkinson's disease.

Parkinson's disease which is the second most prevalent neurodegenerative disorder in the United Stat...

Modelling biochemical oxygen demand using improved neuro-fuzzy approach by marine predators algorithm.

Biochemical oxygen demand (BOD) is one of the most important parameters used for water quality asses...

An Intelligent Rehabilitation Assessment Method for Stroke Patients Based on Lower Limb Exoskeleton Robot.

The 6-min walk distance (6MWD) and the Fugl-Meyer assessment lower-limb subscale (FMA-LE) of the str...

Evaluation of sexual function after robot-assisted radical prostatectomy: A farewell to IIEF questionnaire.

Longevity with localized prostate cancer (PCa) has been achieved, and the contribution of robot-assi...

An appraisal of the performance of AI tools for chronic stroke lesion segmentation.

Automated demarcation of stoke lesions from monospectral magnetic resonance imaging scans is extreme...

Multi-dimensional deep learning drives efficient discovery of novel neuroprotective peptides from walnut protein isolates.

Neurodegenerative diseases, such as Alzheimer's and Parkinson's, are multi-factor induced neurologic...

An alternative method of SNP inclusion to develop a generalized polygenic risk score analysis across Alzheimer's disease cohorts.

INTRODUCTION: Polygenic risk scores (PRSs) have great clinical potential for detecting late-onset di...

Externally validated deep learning model to identify prodromal Parkinson's disease from electrocardiogram.

Little is known about electrocardiogram (ECG) markers of Parkinson's disease (PD) during the prodrom...

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