Neurology

Portable deep-learning decoder for motor imaginary EEG signals based on a novel compact convolutional neural network incorporating spatial-attention mechanism.

Due to high computational requirements, deep-learning decoders for motor imaginary (MI) electroencep...

Contribution of whole slide imaging-based deep learning in the assessment of intraoperative and postoperative sections in neuropathology.

The pathological diagnosis of intracranial germinoma (IG), oligodendroglioma, and low-grade astrocyt...

A direct discretization recurrent neurodynamics method for time-variant nonlinear optimization with redundant robot manipulators.

Discrete time-variant nonlinear optimization (DTVNO) problems are commonly encountered in various sc...

Deep learning-based automated detection and multiclass classification of focal interictal epileptiform discharges in scalp electroencephalograms.

Detection and spatial distribution analyses of interictal epileptiform discharges (IEDs) are importa...

A deep learning-based technique for the diagnosis of epidural spinal cord compression on thoracolumbar CT.

PURPOSE: To develop a deep learning (DL) model for epidural spinal cord compression (ESCC) on CT, wh...

Cerebrovascular super-resolution 4D Flow MRI - Sequential combination of resolution enhancement by deep learning and physics-informed image processing to non-invasively quantify intracranial velocity, flow, and relative pressure.

The development of cerebrovascular disease is tightly coupled to regional changes in intracranial fl...

Revolutionizing the Early Detection of Alzheimer's Disease through Non-Invasive Biomarkers: The Role of Artificial Intelligence and Deep Learning.

Alzheimer's disease (AD) is now classified as a silent pandemic due to concerning current statistics...

Developing DELPHI expert consensus rules for a digital twin model of acute stroke care in the neuro critical care unit.

INTRODUCTION: Digital twins, a form of artificial intelligence, are virtual representations of the p...

Rethinking Saliency Map: A Context-Aware Perturbation Method to Explain EEG-Based Deep Learning Model.

Deep learning is widely used to decode the electroencephalogram (EEG) signal. However, there are few...

Encoding of speech in convolutional layers and the brain stem based on language experience.

Comparing artificial neural networks with outputs of neuroimaging techniques has recently seen subst...

Single-Position L2-S1 Oblique Lumbar Interbody Fusion With Robot-Assisted L2-Ilium Posterior Spinal Fixation: 2-Dimensional Operative Video.

The Use of Machine Learning for Inferencing the Effectiveness of a Rehabilitation Program for Orthopedic and Neurological Patients.

Advance assessment of the potential functional improvement of patients undergoing a rehabilitation p...

Dual-Encoder VAE-GAN With Spatiotemporal Features for Emotional EEG Data Augmentation.

The current data scarcity problem in EEG-based emotion recognition tasks leads to difficulty in buil...

Leveraging Knowledge Graphs and Natural Language Processing for Automated Web Resource Labeling and Knowledge Mobilization in Neurodevelopmental Disorders: Development and Usability Study.

BACKGROUND: Patients and families need to be provided with trusted information more than ever with t...

Explainable classification of Parkinson's disease using deep learning trained on a large multi-center database of T1-weighted MRI datasets.

INTRODUCTION: Parkinson's disease (PD) is a severe neurodegenerative disease that affects millions o...

Aging-related volume changes in the brain and cerebrospinal fluid using artificial intelligence-automated segmentation.

OBJECTIVES: To verify the reliability of the volumes automatically segmented using a new artificial ...

The role of an open artificial intelligence platform in modern neurosurgical education: a preliminary study.

The use of artificial intelligence in neurosurgical education has been growing in recent times. Chat...

Development and Validation of a Deep Learning Predictive Model Combining Clinical and Radiomic Features for Short-Term Postoperative Facial Nerve Function in Acoustic Neuroma Patients.

OBJECTIVE: This study aims to construct and validate a predictable deep learning model associated wi...

Identification of Origin for Spinal Metastases from MR Images: Comparison Between Radiomics and Deep Learning Methods.

OBJECTIVE: To determine whether spinal metastatic lesions originated from lung cancer or from other ...

Benchmarking explanation methods for mental state decoding with deep learning models.

Deep learning (DL) models find increasing application in mental state decoding, where researchers se...

Recognition of Hand Gestures Based on EMG Signals with Deep and Double-Deep Q-Networks.

In recent years, hand gesture recognition (HGR) technologies that use electromyography (EMG) signals...