Neurology

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

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The emerging role of artificial intelligence in neuropathology: Where are we and where do we want to go?

The field of neuropathology, a subspecialty of pathology which studies the diseases affecting the ne...

Resting-State Electroencephalogram Depression Diagnosis Based on Traditional Machine Learning and Deep Learning: A Comparative Analysis.

The global prevalence of Major Depressive Disorder (MDD) is increasing at an alarming rate, undersco...

Deciphering the role of lipid metabolism-related genes in Alzheimer's disease: a machine learning approach integrating Traditional Chinese Medicine.

BACKGROUND: Alzheimer's disease (AD) represents a progressive neurodegenerative disorder characteriz...

Predicting laboratory aspirin resistance in Chinese stroke patients using machine learning models by GP1BA polymorphism.

This study aims to use machine learning model to predict laboratory aspirin resistance (AR) in Chine...

Unmasking Neuroendocrine Prostate Cancer with a Machine Learning-Driven Seven-Gene Stemness Signature That Predicts Progression.

Prostate cancer (PCa) poses a significant global health challenge, particularly due to its progressi...

Enhancing amide proton transfer imaging in ischemic stroke using a machine learning approach with partially synthetic data.

Amide proton transfer (APT) imaging, a technique sensitive to tissue pH, holds promise in the diagno...

3D CNN for neuropsychiatry: Predicting Autism with interpretable Deep Learning applied to minimally preprocessed structural MRI data.

Predictive modeling approaches are enabling progress toward robust and reproducible brain-based mark...

Machine learning reveals prominent spontaneous behavioral changes and treatment efficacy in humanized and transgenic Alzheimer's disease models.

Computer-vision and machine-learning (ML) approaches are being developed to provide scalable, unbias...

Machine learning models of cerebral oxygenation (rcSO) for brain injury detection in neonates with hypoxic-ischaemic encephalopathy.

The present study was designed to test the potential utility of regional cerebral oxygen saturation ...

Machine learning localization to identify the epileptogenic side in mesial temporal lobe epilepsy.

BACKGROUND: Mesial temporal sclerosis (MTS) is the most common pathology associated with drug-resist...

Unlocking therapeutic frontiers: harnessing artificial intelligence in drug discovery for neurodegenerative diseases.

Neurodegenerative diseases (NDs) pose serious healthcare challenges with limited therapeutic treatme...

Attention Induced Dual Convolutional-Capsule Network (AIDC-CN): A deep learning framework for motor imagery classification.

In recent times, Electroencephalography (EEG)-based motor imagery (MI) decoding has garnered signifi...

Big data and artificial intelligence applied to blood and CSF fluid biomarkers in multiple sclerosis.

Artificial intelligence (AI) has meant a turning point in data analysis, allowing predictions of uns...

A systematic review on machine learning approaches in cerebral palsy research.

BACKGROUND: This review aims to explore advances in the field of cerebral palsy (CP) focusing on mac...

Evaluating deep learning techniques for optimal neurons counting and characterization in complex neuronal cultures.

The counting and characterization of neurons in primary cultures have long been areas of significant...

An interpretable and generalizable deep learning model for iEEG-based seizure prediction using prototype learning and contrastive learning.

Epileptic seizure prediction plays a crucial role in enhancing the quality of life for individuals w...

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