Neurology

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

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A temporal-spatial feature fusion network for emotion recognition with individual differences reduction.

PURPOSE: In the context of EEG-based emotion recognition tasks, a conventional strategy involves the...

Artificial intelligence for segmentation and classification in lumbar spinal stenosis: an overview of current methods.

PURPOSE: Lumbar spinal stenosis (LSS) is a frequently occurring condition defined by narrowing of th...

Paradigms of intraoperative neuromonitoring in paediatric thyroid surgery.

The larynx of children and adolescents is still in the developmental phase and the anatomical struct...

Spatially resolved transcriptomics and graph-based deep learning improve accuracy of routine CNS tumor diagnostics.

The diagnostic landscape of brain tumors integrates comprehensive molecular markers alongside tradit...

Unsupervised, piecewise linear decoding enables an accurate prediction of muscle activity in a multi-task brain computer interface.

Creating an intracortical brain computer interface (iBCI) capable of seamless transitions between ta...

The calcitron: A simple neuron model that implements many learning rules via the calcium control hypothesis.

Theoretical neuroscientists and machine learning researchers have proposed a variety of learning rul...

Machine Learning-Based Real-Time Survival Prediction for Gastric Neuroendocrine Carcinoma.

BACKGROUND: This study aimed to develop a dynamic survival prediction model utilizing conditional su...

Machine learning to predict stroke risk from routine hospital data: A systematic review.

PURPOSE: Stroke remains a leading cause of morbidity and mortality. Despite this, current risk strat...

Predictive models of clinical outcome of endovascular treatment for anterior circulation stroke using machine learning.

BACKGROUND AND PURPOSE: Mechanical Thrombectomy (MT) has recently become the standard of care for an...

Artificial intelligence for brain neuroanatomical segmentation in magnetic resonance imaging: A literature review.

PURPOSE: This literature review aims to synthesise current research on the application of artificial...

Molecular Insights into α-Synuclein Fibrillation: A Raman Spectroscopy and Machine Learning Approach.

The aggregation of α-synuclein is crucial to the development of Lewy body diseases, including Parkin...

Preclinical Cognitive Markers of Alzheimer Disease and Early Diagnosis Using Virtual Reality and Artificial Intelligence: Literature Review.

BACKGROUND: This review explores the potential of virtual reality (VR) and artificial intelligence (...

Alzheimer's disease: an integrative bioinformatics and machine learning analysis reveals glutamine metabolism-associated gene biomarkers.

BACKGROUND: Alzheimer's disease (AD), a hallmark of age-related cognitive decline, is defined by its...

Identifying Primary Sites of Spinal Metastases: Expert-Derived Features vs. ResNet50 Model Using Nonenhanced MRI.

BACKGROUND: The spinal column is a frequent site for metastases, affecting over 30% of solid tumor p...

Prediction of Pharmacoresistance in Drug-Naïve Temporal Lobe Epilepsy Using Ictal EEGs Based on Convolutional Neural Network.

Approximately 30%-40% of epilepsy patients do not respond well to adequate anti-seizure medications ...

Utilizing machine learning techniques for EEG assessment in the diagnosis of epileptic seizures in the brain: A systematic review and meta-analysis.

PURPOSE: Advancements in Machine Learning (ML) techniques have revolutionized diagnosing and monitor...

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