Neurology

Detection of parkinson's disease with neuroimaging modalities using machine learning and artificial intelligence: a systematic review.

The application of machine learning (ML) and artificial intelligence (AI) algorithms in medical imaging is an emerging area of interest, particularly in the context of clinical decision-making. Here, we report on the overall performance (i.e., sensitivity, specificity, and accuracy) of commonly used ML/AI techniques including convolutional neural networks (CNNs), support vector machines (SVMs), ra...

Optimization and validation of multiscale feature selection for EEG-based recognition of drivers' negative emotions.

OBJECTIVE: Negative emotions, such as stress and anger, are significant factors leading to dangerous driving behavior. Investigating the impact of these emotions on driving safety is crucial for effective traffic injury prevention. METHODS: Electroencephalography (EEG) is a valuable tool for detecting emotional neural activity due to its high temporal resolution and noninvasive characteristics. Th...

Machine learning and multi-omics integration identifies immunological predictors and mechanistic insights in autoimmune encephalitis.

OBJECTIVE: To develop an interpretable prognostic prediction model for autoimmune encephalitis (AE) using immunological indicators and to investigate ...

Quantifying the impact of a computer-aided diagnostic score on the clinical diagnosis of functional seizures.

OBJECTIVE: The diagnosis of functional/dissociative seizures (FDS) without ictal video-electroencephalography is challenging. The Functional/Dissociat...

Risk Factors and Prediction of Chronic Postsurgical Pain Among Patients With Distal Lower Extremity Fracture: Cohort Analysis.

BACKGROUND: The surgical interventions aimed at fracture repair are often accompanied by chronic postsurgical pain (CPSP), which is associated with de...

Nerves Stimulates Crosstalk between Gastric Cancer and Group 3 Innate Lymphoid Cells to Enhance Immunosuppression.

The immunosuppressive tumor microenvironment (TME) enables cancer cells to evade clinical immunotherapies. Neural networks are vital components of the...

Development of a cloud-based estimator for analysing the disc-fovea angle in fundus images using a stacking ensemble model.

PURPOSE: To develop a cloud-based software for analysing the disc-fovea angle (DFA) in fundus images using the stacking ensemble model and to evaluate...

Quantum inspired wavelet and Fourier feature fusion for EEG based epilepsy and seizure detection.

The electroencephalography (EEG) signals are the cheapest approach to study the brain information, commonly used for epilepsy and seizure detection. T...

Digital markers and phenotypes of rest-activity rhythms in people with advanced dementia using real-time location data.

BACKGROUND: Disrupted sleep and circadian rhythms in dementia affect rest-activity patterns and impact quality of life, safety, and caregiver burden. ...

Externally validated knowledge-based planning model for machine learning-assisted radiation therapy treatment of high-grade glioma.

We have trained and externally validated a knowledge-based planning model for radiation therapy planning in the setting of high-grade glioma. Model pe...

Intrinsically disordered proteins and liquid-liquid phase separation in drug discovery.

Intrinsically disordered proteins (IDPs) and their involvement in liquid-liquid phase separation (LLPS) have reshaped our understanding of how cells o...

Initial diagnosis and management of acute ischemic stroke: recent update and future direction.

Acute ischemic stroke (AIS) outcomes depend critically on rapid, accurate early diagnosis in the emergency department. Traditional prehospital tools a...

Association between cognitive status and structural brain changes in Alzheimer's disease: Clinical implication of lightweight deep learning-aided diagnosis.

BACKGROUND: The complex brain changes involved in Alzheimer's disease (AD) development constitute a high-dimensional nonlinear feature space where dee...

Artificial Intelligence in Neurology and Neuro-Ophthalmology: Current Applications and Future Directions.

Artificial intelligence (AI) is revolutionizing neuro-ophthalmology by enhancing diagnostic accuracy and clinical decision-making. Techniques like dee...

Machine learning for discovery of clinical pain biomarkers following spinal cord injury.

Chronic pain is highly prevalent in patients with spinal cord injury (SCI) and further degrades the quality of life in individuals already struggling ...

CT-free attenuation and scatter correction of [11C]CFT brain PET using a Bi-directional matching network.

Quantitative PET imaging requires accurate attenuation and scatter correction (ASC), but the standard CT-based method introduces additional radiation ...

Identifying endoplasmic reticulum stress-related key genes of vascular dementia using bioinformatics and biological experiments.

Vascular dementia (VaD) is the second most common type of dementia, yet its pathogenesis is not fully understood, and effective diagnostic and therape...

Risk Factors and an Interpretable Machine Learning Model for Predicting Spinal Epidural Lipomatosis: A Multicenter Study.

STUDY DESIGN: A retrospective multicenter study. OBJECTIVE: To identify independent risk factors for spinal epidural lipomatosis (SEL) and to develop ...

Advancing precision psychiatry: Machine learning integration with neuroimaging for early detection and diagnosis of Obsessive-Compulsive Disorder.

Early detection of Obsessive-Compulsive Disorder (OCD), a chronic mental health condition characterized by intrusive thoughts and repetitive behaviors...

Neural signatures of major depressive disorder with comorbid insomnia: Low gamma PoCG.R-INS.L connectivity as a potential biomarker.

BACKGROUND: Insomnia is a common and distressing symptom in major depressive disorder (MDD). However, research focusing on the neurobiological mechani...