Neurology

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

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Deep learning-based automatic segmentation of cerebral infarcts on diffusion MRI.

We explored effects of (1) training with various sample sizes of multi-site vs. single-site training...

A deep learning approach for quantifying CT perfusion parameters in stroke.

. Computed tomography perfusion (CTP) imaging is widely used for assessing acute ischemic stroke. Ho...

Neuro-Modulation Analysis Based on Muscle Synergy Graph Neural Network in Human Locomotion.

The coordination of muscles in human locomotion is commonly understood as the integration of motor m...

Multi-scale convolutional transformer network for motor imagery brain-computer interface.

Brain-computer interface (BCI) systems allow users to communicate with external devices by translati...

Unsupervised alignment in neuroscience: Introducing a toolbox for Gromov-Wasserstein optimal transport.

BACKGROUND: Understanding how sensory stimuli are represented across different brains, species, and ...

Strategies to Improve the Robustness and Generalizability of Deep Learning Segmentation and Classification in Neuroimaging.

Artificial Intelligence (AI) and deep learning models have revolutionized diagnosis, prognostication...

Leveraging pretrained language models for seizure frequency extraction from epilepsy evaluation reports.

Seizure frequency is essential for evaluating epilepsy treatment, ensuring patient safety, and reduc...

A dual-branch hybrid network with bilateral-difference awareness for collateral scoring on CT angiography of acute ischemic stroke patients.

Acute ischemic stroke (AIS) patients with good collaterals tend to have better outcomes after endova...

c-Triadem: A constrained, explainable deep learning model to identify novel biomarkers in Alzheimer's disease.

Alzheimer's disease (AD) is a neurodegenerative disorder that requires early diagnosis for effective...

Hybrid of DSR-GAN and CNN for Alzheimer disease detection based on MRI images.

In this paper, we propose a deep super-resolution generative adversarial network (DSR-GAN) combined ...

Neuroimaging-derived biological brain age and its associations with glial reactivity and synaptic dysfunction cerebrospinal fluid biomarkers.

Magnetic resonance Imaging (MRI)-derived brain-age prediction is a promising biomarker of biological...

The future of Alzheimer's disease risk prediction: a systematic review.

BACKGROUND: Alzheimer's disease is the most prevalent kind of age-associated dementia among older ad...

A diagnosis and prediction algorithm for juvenile myoclonic epilepsy based on clinical and quantitative EEG features.

OBJECTIVE: To develop an objective ensemble machine learning model combining clinical features and q...

Machine learning-based differentiation of schizophrenia and bipolar disorder using multiscale fuzzy entropy and relative power from resting-state EEG.

Schizophrenia (SZ) and bipolar disorder (BD) pose diagnostic challenges due to overlapping clinical ...

Automated phenotyping of mild cognitive impairment and Alzheimer's disease and related dementias using electronic health records.

OBJECTIVES: Unstructured and structured data in electronic health records (EHR) are a rich source of...

Deep Learning Approach Readily Differentiates Papilledema, Non-Arteritic Anterior Ischemic Optic Neuropathy, and Healthy Eyes.

OBJECTIVE: Deep learning (DL) has been used in differentiating a range of ophthalmic conditions. We ...

CASCADE-FSL: Few-shot learning for collateral evaluation in ischemic stroke.

Assessing collateral circulation is essential in determining the best treatment for ischemic stroke ...

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