Neurology

Robotic-navigated assistance in spine surgery.

The aim of this study was to assess the accuracy of pedicle screw placement, as well as intraoperati...

Wide and deep learning based approaches for classification of Alzheimer's disease using genome-wide association studies.

The increasing incidence of Alzheimer's disease (AD) has been leading towards a significant growth i...

Enabling Scientific Reproducibility through FAIR Data Management: An ontology-driven deep learning approach in the NeuroBridge Project.

Scientific reproducibility that effectively leverages existing study data is critical to the advance...

Efficacy of Robot-Assisted and Virtual Reality Interventions on Balance, Gait, and Daily Function in Patients With Stroke: A Systematic Review and Network Meta-analysis.

OBJECTIVE: This study aimed to evaluate the comparative effectiveness and ranking of robot-assisted ...

Gait training with a wearable powered robot during stroke rehabilitation: a randomized parallel-group trial.

BACKGROUND: We have developed a wearable rehabilitation robot, "curara®," and examined its immediate...

Neurophysiology, Neuropsychology, and Epilepsy, in 2022: Hills We Have Climbed and Hills Ahead. Neurophysiology in epilepsy.

Since the discovery of the human electroencephalogram (EEG), neurophysiology techniques have become ...

The role of artificial intelligence in headache medicine: Potential and peril.

Benefits and implications of integrating artificial intelligence in manuscripts submitted to Headache.

A novel performance scoring quantification framework for stress test set-ups.

Stress tests, e.g., the cardiac stress test, are standard clinical screening tools aimed to unmask c...

Automatized offline and online exploration to achieve a target dynamics in biohybrid neural circuits built with living and model neurons.

Biohybrid circuits of interacting living and model neurons are an advantageous means to study neural...

Performance-Based Robotic Training in Individuals with Subacute Stroke: Differences between Responders and Non-Responders.

The high variability of upper limb motor recovery with robotic training (RT) in subacute stroke unde...

Implementing PainChek and PARO to Support Pain Assessment and Management in Residents with Dementia: A Qualitative Study.

BACKGROUND: Pain is a common problem but often undiagnosed and untreated in people with dementia.

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...