Neurology

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

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Deep learning-based automated lesion segmentation on mouse stroke magnetic resonance images.

Magnetic resonance imaging (MRI) is widely used for ischemic stroke lesion detection in mice. A chal...

Deep-learning based detection of vessel occlusions on CT-angiography in patients with suspected acute ischemic stroke.

Swift diagnosis and treatment play a decisive role in the clinical outcome of patients with acute is...

Deep-Learning-Based Model for the Prediction of Cancer-Specific Survival in Patients with Spinal Chordoma.

OBJECTIVE: Spinal chordomas are locally aggressive and frequently recurrent tumors with a poor progn...

Impairments of the ipsilesional upper-extremity in the first 6-months post-stroke.

BACKGROUND: Ipsilesional motor impairments of the arm are common after stroke. Previous studies have...

Neuroendoscopy-Assisted Minimal Invasive Management of Chiari 1 Malformation.

 The aim this study is to present the results of the minimal invasive neuroendoscopic-assisted syst...

Value Proposition of FDA-Approved Artificial Intelligence Algorithms for Neuroimaging.

PURPOSE: The number of FDA-cleared artificial intelligence (AI) algorithms for neuroimaging has grow...

Robotic Interventional Neuroradiology: Progress, Challenges, and Future Prospects.

Advances in robotic technology have improved standard techniques in numerous surgical and endovascul...

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 ...

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