Neurology

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

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Developing multifactorial dementia prediction models using clinical variables from cohorts in the US and Australia.

Existing dementia prediction models using non-neuroimaging clinical measures have been limited in th...

Model-agnostic meta-learning for EEG-based inter-subject emotion recognition.

. Developing an efficient and generalizable method for inter-subject emotion recognition from neural...

Machine Learning-Based Diagnosis of Chronic Subjective Tinnitus With Altered Cognitive Function: An Event-Related Potential Study.

OBJECTIVES: Due to the absence of objective diagnostic criteria, tinnitus diagnosis primarily relies...

Machine learning-driven identification of critical gene programs and key transcription factors in migraine.

BACKGROUND: Migraine is a complex neurological disorder characterized by recurrent episodes of sever...

Machine learning validation of the AVAS classification compared to ultrasound mapping in a multicentre study.

The Arteriovenous Access Stage (AVAS) classification simplifies information about suitability of ves...

Efficient recognition of Parkinson's disease mice on stepping characters with CNN.

Parkinson's disease (PD), as the second most prevalent neurodegenerative disorder worldwide, impacts...

Machine Learning for the Early Prediction of Delayed Cerebral Ischemia in Patients With Subarachnoid Hemorrhage: Systematic Review and Meta-Analysis.

BACKGROUND: Delayed cerebral ischemia (DCI) is a primary contributor to death after subarachnoid hem...

Your brain on art, nature, and meditation: a pilot neuroimaging study.

OBJECTIVES: Exposure to art, nature, or meditation, all transcending human experiences, has benefici...

Flexible Neuromorphic Electronics for Wearable Near-Sensor and In-Sensor Computing Systems.

Flexible neuromorphic architectures that emulate biological cognitive systems hold great promise for...

PIDGN: An explainable multimodal deep learning framework for early prediction of Parkinson's disease.

BACKGROUND: Parkinson's disease (PD), the second most common neurodegenerative disease in the world,...

AI-based assessment of longitudinal multiple sclerosis MRI: Strengths and weaknesses in clinical practice.

OBJECTIVES: In Multiple Sclerosis (MS) cerebral MRI is essential for disease and treatment monitorin...

Claude, ChatGPT, Copilot, and Gemini performance versus students in different topics of neuroscience.

Despite extensive studies on large language models and their capability to respond to questions from...

Clinical feasibility of deep learning-driven magnetic resonance angiography collateral map in acute anterior circulation ischemic stroke.

To validate the clinical feasibility of deep learning-driven magnetic resonance angiography (DL-driv...

Multidimensional free shape-morphing flexible neuromorphic devices with regulation at arbitrary points.

Biological neural systems seamlessly integrate perception and action, a feat not efficiently replica...

Alzheimer's disease diagnosis using rhythmic power changes and phase differences: a low-density EEG study.

OBJECTIVES: The future emergence of disease-modifying treatments for dementia highlights the urgent ...

Method for assessing visual saliency in children with cerebral/cortical visual impairment using generative artificial intelligence.

Cerebral/cortical visual impairment (CVI) is a leading cause of pediatric visual impairment in the U...

Neurological history both twinned and queried by generative artificial intelligence.

BACKGROUND AND OBJECTIVES: We propose the use of GPT-4 to facilitate initial history-taking in neuro...

Diagnosing Epilepsy with Normal Interictal EEG Using Dynamic Network Models.

OBJECTIVE: Whereas a scalp electroencephalogram (EEG) is important for diagnosing epilepsy, a single...

Identifying multilayer network hub by graph representation learning.

The recent advances in neuroimaging technology allow us to understand how the human brain is wired i...

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