Neurology

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

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Deep learning model for automated diagnosis of degenerative cervical spondylosis and altered spinal cord signal on MRI.

BACKGROUND CONTEXT: A deep learning (DL) model for degenerative cervical spondylosis on MRI could en...

A Lightweight Convolutional Neural Network-Reformer Model for Efficient Epileptic Seizure Detection.

A real-time and reliable automatic detection system for epileptic seizures holds significant value i...

MMF-NNs: Multi-modal Multi-granularity Fusion Neural Networks for brain networks and its application to epilepsy identification.

Structural and functional brain networks are generated from two scan sequences of magnetic resonance...

Joint self-supervised and supervised contrastive learning for multimodal MRI data: Towards predicting abnormal neurodevelopment.

The integration of different imaging modalities, such as structural, diffusion tensor, and functiona...

Machine-learning-based models for the optimization of post-cervical spinal laminoplasty outpatient follow-up schedules.

BACKGROUND: Patients undergo regular clinical follow-up after laminoplasty for cervical myelopathy. ...

Convolutional neural network based detection of early stage Parkinson's disease using the six minute walk test.

The heterogeneity of Parkinson's disease (PD) presents considerable challenges for accurate diagnosi...

Machine learning and biological validation identify sphingolipids as potential mediators of paclitaxel-induced neuropathy in cancer patients.

BACKGROUND: Chemotherapy-induced peripheral neuropathy (CIPN) is a serious therapy-limiting side eff...

Machine-learning-based classification of obstructive sleep apnea using 19-channel sleep EEG data.

OBJECTIVE: This study aimed to investigate the neurophysiological effects of obstructive sleep apnea...

Pain Assessment for Patients with Dementia and Communication Impairment: Feasibility Study of the Usage of Artificial Intelligence-Enabled Wearables.

BACKGROUND: Recent studies on machine learning have shown the potential to provide new methods with ...

Translational Connectomics: overview of machine learning in macroscale Connectomics for clinical insights.

Connectomics is a neuroscience paradigm focused on noninvasively mapping highly intricate and organi...

Late feature fusion using neural network with voting classifier for Parkinson's disease detection.

Parkinson's disease (PD) is classified as a neurological, progressive illness brought on by cell dea...

Explainability of CNN-based Alzheimer's disease detection from online handwriting.

With over 55 million people globally affected by dementia and nearly 10 million new cases reported a...

Pattern recognition using spiking antiferromagnetic neurons.

Spintronic devices offer a promising avenue for the development of nanoscale, energy-efficient artif...

An Experimental and Clinical Physiological Signal Dataset for Automated Pain Recognition.

Access to large amounts of data is essential for successful machine learning research. However, ther...

Early Detection of Parkinson's Disease Using Deep NeuroEnhanceNet With Smartphone Walking Recordings.

With the development of digital medical technology, ubiquitous smartphones are emerging as valuable ...

Cortical ROI Importance Improves MI Decoding From EEG Using Fused Light Neural Network.

Decoding motor imagery (MI) using deep learning in cortical level has potential in brain computer in...

Operant Conditioning Neuromorphic Circuit With Addictiveness and Time Memory for Automatic Learning.

Most operant conditioning circuits predominantly focus on simple feedback process, few studies consi...

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