Neurology

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

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Organic iontronic memristors for artificial synapses and bionic neuromorphic computing.

To tackle the current crisis of Moore's law, a sophisticated strategy entails the development of mul...

Exploring new horizons: Emerging therapeutic strategies for pediatric stroke.

Pediatric stroke presents unique challenges, and optimizing treatment strategies is essential for im...

A journey from molecule to physiology and tools for drug discovery targeting the transient receptor potential vanilloid type 1 (TRPV1) channel.

The heat and capsaicin receptor TRPV1 channel is widely expressed in nerve terminals of dorsal root ...

Enhancing foveal avascular zone analysis for Alzheimer's diagnosis with AI segmentation and machine learning using multiple radiomic features.

We propose a hybrid technique that employs artificial intelligence (AI)-based segmentation and machi...

Developing an ontology of non-pharmacological treatment for emotional and mood disturbances in dementia.

Emotional and mood disturbances are common in people with dementia. Non-pharmacological intervention...

Resource-Efficient Neural Network Architectures for Classifying Nerve Cuff Recordings on Implantable Devices.

BACKGROUND: Closed-loop functional electrical stimulation can use recorded nerve signals to create i...

Annotated dataset for training deep learning models to detect astrocytes in human brain tissue.

Astrocytes, a type of glial cell, significantly influence neuronal function, with variations in morp...

Serum Lidocaine Levels in Adult Patients Undergoing Cardiac Surgery With del Nido Cardioplegia.

BACKGROUND: Lidocaine in del Nido cardioplegia solution prolongs the refractory period of cardiomyoc...

Generalizing Upper Limb Force Modeling With Transfer Learning: A Multimodal Approach Using EMG and IMU for New Users and Conditions.

In the field of EMG-based force modeling, the ability to generalize models across individuals could ...

Deep Learning-Based Assessment Model for Real-Time Identification of Visual Learners Using Raw EEG.

Automatic identification of visual learning style in real time using raw electroencephalogram (EEG) ...

Deep Learning-Based Identification Algorithm for Transitions Between Walking Environments Using Electromyography Signals Only.

Although studies on terrain identification algorithms to control walking assistive devices have been...

NeuroAIreh@b: an artificial intelligence-based methodology for personalized and adaptive neurorehabilitation.

Cognitive impairments are a prevalent consequence of acquired brain injury, dementia, and age-relate...

Harnessing Transfer Learning for Dementia Prediction: Leveraging Sex-Different Mild Cognitive Impairment Prognosis.

This paper presents a machine learning-based prediction for dementia, leveraging transfer learning t...

A generalizable physiological model for detection of Delayed Cerebral Ischemia using Federated Learning.

Delayed cerebral ischemia (DCI) is a complication seen in patients with subarachnoid hemorrhage stro...

Artificial intelligence-based decision support software to improve the efficacy of acute stroke pathway in the NHS: an observational study.

INTRODUCTION: In a drip-and-ship model for endovascular thrombectomy (EVT), early identification of ...

Deep learning algorithm for fully automated measurement of sagittal balance in adult spinal deformity.

AIM: Deep learning (DL) algorithms can be used for automated analysis of medical imaging. The aim of...

A Machine Learning Approach to Predict Post-stroke Fatigue. The Nor-COAST study.

OBJECTIVE: This study aimed to predict fatigue 18 months post-stroke by utilizing comprehensive data...

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