Neurology

HEMAsNet: A Hemisphere Asymmetry Network Inspired by the Brain for Depression Recognition From Electroencephalogram Signals.

Depression is a prevalent mental disorder that affects a significant portion of the global population. Despite recent advancements in EEG-based depression recognition models rooted in machine learning and deep learning approaches, many lack comprehensive consideration of depression's pathogenesis, leading to limited neuroscientific interpretability. To address these issues, we propose a hemisphere...

The application value of Rs-fMRI-based machine learning models for differentiating mild cognitive impairment from Alzheimer's disease: a systematic review and meta-analysis.

BACKGROUND: Various machine learning (ML) models based on resting-state functional MRI (Rs-fMRI) have been developed to facilitate differential diagnosis of mild cognitive impairment (MCI) and Alzheimer's disease (AD). However, the diagnostic accuracy of such models remains understudied. Therefore, we conducted this systematic review and meta-analysis to explore the diagnostic accuracy of Rs-fMRI-...

Social anxiety prediction based on ERP features: A deep learning approach.

BACKGROUND: Social Anxiety Disorder is traditionally diagnosed using subjective scales that may lack accuracy. Recently, EEG technology has gained imp...

Cross-subject emotion recognition in brain-computer interface based on frequency band attention graph convolutional adversarial neural networks.

BACKGROUND: Emotion is an important area in neuroscience. Cross-subject emotion recognition based on electroencephalogram (EEG) data is challenging du...

Advancing Tau PET Quantification in Alzheimer Disease with Machine Learning: Introducing THETA, a Novel Tau Summary Measure.

Alzheimer disease (AD) exhibits spatially heterogeneous 3- or 4-repeat tau deposition across participants. Our overall goal was to develop an automate...

SSGCNet: A Sparse Spectra Graph Convolutional Network for Epileptic EEG Signal Classification.

In this article, we propose a sparse spectra graph convolutional network (SSGCNet) for epileptic electroencephalogram (EEG) signal classification. The...

Higher Order Polynomial Transformer for Fine-Grained Freezing of Gait Detection.

Freezing of Gait (FoG) is a common symptom of Parkinson's disease (PD), manifesting as a brief, episodic absence, or marked reduction in walking, desp...

Brain Emotion Perception Inspired EEG Emotion Recognition With Deep Reinforcement Learning.

Inspired by the well-known Papez circuit theory and neuroscience knowledge of reinforcement learning, a double dueling deep Q network (DQN) is built i...

Cost-Sensitive Weighted Contrastive Learning Based on Graph Convolutional Networks for Imbalanced Alzheimer's Disease Staging.

Identifying the progression stages of Alzheimer's disease (AD) can be considered as an imbalanced multi-class classification problem in machine learni...

A retrospective evaluation of the potential of ChatGPT in the accurate diagnosis of acute stroke.

PURPOSE: Stroke is a neurological emergency requiring rapid, accurate diagnosis to prevent severe consequences. Early diagnosis is crucial for reducin...

SCINet: A Segmentation and Classification Interaction CNN Method for Arteriosclerotic Retinopathy Grading.

As a common disease, cardiovascular and cerebrovascular diseases pose a great harm threat to human wellness. Even using advanced and comprehensive tre...

Auxiliary diagnostic method of Parkinson's disease based on eye movement analysis in a virtual reality environment.

Eye movement dysfunction is one of the non-motor symptoms of Parkinson's disease (PD). An accurate analysis method for eye movement is an effective wa...

Impact of acquisition area on deep-learning-based glaucoma detection in different plexuses in OCTA.

Glaucoma is a group of neurodegenerative diseases that can lead to irreversible blindness. Yet, the progression can be slowed down if diagnosed and tr...

Automated classification of mandibular canal in relation to third molar using CBCT images.

BACKGROUND: Dental radiology has significantly benefited from cone-beam computed tomography (CBCT) because of its compact size and low radiation expos...

Development of machine learning-based models for predicting risk factors in acute cerebral infarction patients: a clinical retrospective study.

OBJECTIVES: The aim of this study was to develop machine learning-based models for predicting acute cerebral infarction (ACI) in patients.

Can deep learning classify cerebral ultrasound images for the detection of brain injury in very preterm infants?

OBJECTIVES: Cerebral ultrasound (CUS) is the main imaging screening tool in preterm infants. The aim of this work is to develop deep learning (DL) mod...

Development and External Validation of a Motor Intention-Integrated Prediction Model for Upper Extremity Motor Recovery After Intention-Driven Robotic Hand Training for Chronic Stroke.

OBJECTIVE: To derive and validate a prediction model for minimal clinically important differences (MCIDs) in upper extremity (UE) motor function after...

The Future of Sustainable Neurosurgery: Is a Moonshot Plan for Artificial Intelligence and Robot-Assisted Surgery Possible in Japan?

Japanese neurosurgery faces challenges such as a declining number of neurosurgeons and their concentration in urban areas. Particularly in rural areas...

Construction and verification of a machine learning-based prediction model of deep vein thrombosis formation after spinal surgery.

BACKGROUND: Deep vein thromboembolism (DVT) is a common postoperative complication with high morbidity and mortality rates. However, the safety and ef...

Artificial intelligence and telemedicine in epilepsy and EEG: A narrative review.

The emergence of telemedicine and artificial intelligence (AI) has set the stage for a possible revolution in the future of medicine and neurology inc...