Neurology

Artificial intelligence and social media: (Appropriately) harnessing headache medicine's new arsenal in the 21st century.

A hybrid 1D CNN-BiLSTM model for epileptic seizure detection using multichannel EEG feature fusion.

Epilepsy, a chronic non-communicable disease is characterized by repeated unprovoked seizures, which...

Resolution of tonic concentrations of highly similar neurotransmitters using voltammetry and deep learning.

With advances in our understanding regarding the neurochemical underpinnings of neurological and psy...

Deep learning models for ischemic stroke lesion segmentation in medical images: A survey.

This paper provides a comprehensive review of deep learning models for ischemic stroke lesion segmen...

Construction and validation of an algorithm to separate focal and generalised epilepsy using clinical variables: A comparison of machine learning approaches.

PURPOSE: Epilepsy type, whether focal or generalised, is important in deciding anti-seizure medicati...

Enhanced Spatial Fuzzy C-Means Algorithm for Brain Tissue Segmentation in T1 Images.

Magnetic Resonance Imaging (MRI) plays an important role in neurology, particularly in the precise s...

Attention-based convolutional neural network with multi-modal temporal information fusion for motor imagery EEG decoding.

Convolutional neural network (CNN) has been widely applied in motor imagery (MI)-based brain compute...

Autism spectrum disorder diagnosis with EEG signals using time series maps of brain functional connectivity and a combined CNN-LSTM model.

BACKGROUND AND OBJECTIVE: People with autism spectrum disorder (ASD) often have cognitive impairment...

Combined blood Neurofilament light chain and third ventricle width to differentiate Progressive Supranuclear Palsy from Parkinson's Disease: A machine learning study.

INTRODUCTION: Differentiating Progressive Supranuclear Palsy (PSP) from Parkinson's Disease (PD) may...

A Mouth and Tongue Interactive Device to Control Wearable Robotic Limbs in Tasks where Human Limbs Are Occupied.

The Wearable Robotic Limb (WRL) is a type of robotic arm worn on the human body, aiming to enhance t...

Study on Gesture Recognition Method with Two-Stream Residual Network Fusing sEMG Signals and Acceleration Signals.

Currently, surface EMG signals have a wide range of applications in human-computer interaction syste...

Transfer learning and self-distillation for automated detection of schizophrenia using single-channel EEG and scalogram images.

Schizophrenia (SZ) has been acknowledged as a highly intricate mental disorder for a long time. In f...

Machine Learning-Based Perivascular Space Volumetry in Alzheimer Disease.

OBJECTIVES: Impaired perivascular clearance has been suggested as a contributing factor to the patho...

Application of interpretable machine learning algorithms to predict acute kidney injury in patients with cerebral infarction in ICU.

BACKGROUND: Acute kidney injury (AKI) is not only a complication but also a serious threat to patien...

How robot-assisted gait training affects gait ability, balance and kinematic parameters after stroke: a systematic review and meta-analysis.

INTRODUCTION: Gait ability is often cited by stroke survivors. Robot-assisted gait training (RAGT) c...

Physics-Informed Transfer Learning to Enhance Sleep Staging.

OBJECTIVE: At-home sleep staging using wearable medical sensors poses a viable alternative to in-hos...

Streamlining neuroradiology workflow with AI for improved cerebrovascular structure monitoring.

Radiological imaging to examine intracranial blood vessels is critical for preoperative planning and...

External evaluation of a deep learning-based approach for automated brain volumetry in patients with huntington's disease.

A crucial step in the clinical adaptation of an AI-based tool is an external, independent validation...

Building Uniformly Structured Polymer Memristors via a 2D Conjugation Strategy for Neuromorphic Computing.

Polymer memristors represent a highly promising avenue for the advancement of next-generation comput...

Classification of mental workload using brain connectivity and machine learning on electroencephalogram data.

Mental workload refers to the cognitive effort required to perform tasks, and it is an important fac...

Artificial Intelligence Assistance for the Measurement of Full Alignment Parameters in Whole-Spine Lateral Radiographs.

BACKGROUND: Measuring spinal alignment with radiological parameters is essential in patients with sp...