Neurology

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

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Interpretable machine learning-driven biomarker identification and validation for Alzheimer's disease.

Alzheimer's disease (AD) is a neurodegenerative disorder characterized by limited effective treatmen...

Emotion recognition using multi-scale EEG features through graph convolutional attention network.

Emotion recognition via electroencephalogram (EEG) signals holds significant promise across various ...

A novel way to use cross-validation to measure connectivity by machine learning allows epilepsy surgery outcome prediction.

The rate of success of epilepsy surgery, ensuring seizure-freedom, is limited by the lack of epilept...

AI-Powered Neurogenetics: Supporting Patient's Evaluation with Chatbot.

BACKGROUND/OBJECTIVES: Artificial intelligence and large language models like ChatGPT and Google's G...

Highly Responsive Robotic Prosthetic Hand Control Considering Electrodynamic Delay.

As robots become increasingly integrated into human society, the importance of human-machine interfa...

Prediction of Composite Clinical Outcomes for Childhood Neuroblastoma Using Multi-Omics Data and Machine Learning.

Neuroblastoma is a common malignant tumor in childhood that seriously endangers the health and lives...

Preictal period optimization for deep learning-based epileptic seizure prediction.

. Accurate seizure prediction could prove critical for improving patient safety and quality of life ...

Decoding neuronal networks: A Reservoir Computing approach for predicting connectivity and functionality.

In this study, we address the challenge of analyzing electrophysiological measurements in neuronal n...

Predicting the likelihood of readmission in patients with ischemic stroke: An explainable machine learning approach using common data model data.

BACKGROUND: Ischemic stroke affects 15 million people worldwide, causing five million deaths annuall...

Evaluation of cerebral blood flow after subarachnoid hemorrhage using near-field coupling and machine learning.

BackgroundBedside continuous monitoring of cerebral blood flow (CBF) has significant implications in...

Proteome profiling of cerebrospinal fluid using machine learning shows a unique protein signature associated with APOE4 genotype.

Proteome changes associated with APOE4 variant carriage that are independent of Alzheimer's disease ...

Development of Machine-learning Model to Predict Anticoagulant Use and Type in Geriatric Traumatic Brain Injury Using Coagulation Parameters.

This study aimed to investigate the patterns of anticoagulation therapy and coagulation parameters a...

Optimizing autonomous artificial intelligence diagnostics for neuro-ocular health in space missions.

Spaceflight-Associated Neuro-Ocular Syndrome (SANS) presents a critical risk in long-duration missio...

Effect of artificial intelligence-based video-game system on dysphagia in patients with stroke: A randomized controlled trial.

BACKGROUND AND AIMS: Post-stroke dysphagia is highly prevalent and causes complication. While video ...

A wrapper method for finding optimal subset of multimodal Magnetic Resonance Imaging sequences for ischemic stroke lesion segmentation.

Multimodal data, while being information-rich, contains complementary as well as redundant informati...

Epileptic seizure detection in EEG signals via an enhanced hybrid CNN with an integrated attention mechanism.

Epileptic seizures, a prevalent neurological condition, necessitate precise and prompt identificatio...

Deep learning-based automated tool for diagnosing diabetic peripheral neuropathy.

BACKGROUND: Diabetic peripheral neuropathy (DPN) is a common complication of diabetes, and its early...

Multi-modal cross-domain self-supervised pre-training for fMRI and EEG fusion.

Neuroimaging techniques including functional magnetic resonance imaging (fMRI) and electroencephalog...

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