Neurology

Prediction of recurrent ischemic stroke using machine learning from real-world data.

BACKGROUND: Recurrent ischemic stroke (RIS) is a significant challenge in Malaysia, affecting approximately 33% of patients. However, studies using artificial intelligence (AI) to predict this event using real-world data remain very limited. This study aimed to develop and evaluate Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and RUSBoost models for predicting recurrent ischemic stroke ...

Generalized FitzHugh-Nagumo equations with Caputo gH-differentiability: A novel fuzzy fractional approach to digital memristor networks.

The fuzzy fractional generalized FitzHugh-Nagumo differential equations (FFGFH-NDEs) is a well-known and generalized model that plays a significant role in biological systems, including complex synchronization in brain networks, cardiac dynamics, propagation of signals through nerve impulses, and digital circuit theory. The analytical study of the FFGFH-NDEs is more complex and difficult to deal w...

A knowledge-driven self-supervised learning method for enhancing EEG-based emotion recognition.

Emotion recognition brain-computer interface (BCI) using electroencephalography (EEG) is crucial for human-computer interaction, medicine, and neurosc...

Altered entropy modulation in bipolar disorder: EEG entropy measures during steady-state auditory entrainment.

BACKGROUND: Non-linear neural dynamics reflect the inherent complexity of brain activity and are increasingly recognized as important indicators of ne...

DeepVBM: A fully automatic and efficient voxel-based morphometry via deep learning-based segmentation and registration methods.

Voxel-based morphometry (VBM) using T1-weighted magnetic resonance imaging is a pivotal tool for assessing brain structure and identifying subtle morp...

Machine learning-assisted screening of small-molecule drugs for suppressing protein aggregation and ROS generation based on ECL and CV dual-mode signals amplified by DNA.

Screening of small-molecule drugs to suppress both protein aggregation and reactive oxygen species (ROS) generation is critical for developing therapi...

Uncertainty in deep learning for EEG under dataset shifts.

As artificial intelligence (AI) is increasingly integrated into medical diagnostics, it is essential that predictive models provide not only accurate ...

A machine learning model for prediction of risk of dementia superimposed on delirium in intensive care patients.

BACKGROUND: The objective of this study was to construct a predictive model using multiple machine learning algorithms to predict the risk of dementia...

Enhancing upper limb motor recovery prediction after acute stroke using EEG and subacute data.

Electroencephalography (EEG) has shown promise in assessing and monitoring functional recovery in stroke survivors, but its utility in predicting uppe...

Artificial Intelligence-based detection of neuropsychiatric lupus: an exploratory meta-analysis of neuroimaging and multimodal biomarker models.

Neuropsychiatric systemic lupus erythematosus (NPSLE) remains challenging to diagnose because of heterogeneous clinical presentations, nonspecific fin...

Toward early warning of unsafe behavior of excavator operators under time pressure: experimental evidence and EEG-based detection via RCF-IncepLite model.

Time pressure can impair the cognitive functioning of excavator operators, thereby increasing unsafe behaviors and elevating the likelihood of acciden...

MRI neuroimaging-based Alzheimer's disease stage classification using deep neural network with convolutional block attention module and GAN-style noise injection.

Millions of individuals worldwide suffer from Alzheimer's disease (AD), a chronic, incurable neurological disorder. For the longevity of people, a com...

Amyloid-β and phosphorylated-tau accumulation in retrosplenial cortex of young Alzheimer's disease model: Impact on network activity.

BackgroundThe retrosplenial cortex (RSC) is a cortical area that functions as a key component of the core network of brain regions involved in cogniti...

Diagnostic Accuracy of Cerebrospinal Fluid Presepsin vs. Procalcitonin in Post-Neurosurgical Bacterial Ventriculitis/Meningitis: A Machine Learning Analytical Approach.

This study aimed to evaluate the diagnostic accuracy of cerebrospinal fluid presepsin and procalcitonin in patients who had undergone neurosurgery bet...

Simplifying Depression Diagnosis: Single-Channel EEG and Deep Learning Approaches.

Major depressive disorder (MDD) or depression is a chronic mental illness that significantly impacts individuals' well-being and is often diagnosed at...

Developing a Multimodal Screening Algorithm for Mild Cognitive Impairment and Early Dementia in Home Health Care: Protocol for a Cross-Sectional Case-Control Study Using Speech Analysis, Large Language Models, and Electronic Health Records.

BACKGROUND: Mild cognitive impairment and early dementia (MCI-ED) are frequently unrecognized in routine care, particularly in home health care (HHC),...

The Accuracy of Artificial Intelligence Models in Carpal Tunnel Diagnosis: A Systematic Review and Meta-analysis.

BACKGROUND: Artificial intelligence (AI) has been integrated into diagnostic modalities like nerve conduction studies (NCS) and ultrasound (US) to imp...

Machine Learning Approach to Predict Pain Outcomes Following Primary and Secondary Targeted Muscle Reinnervation in Amputees.

INTRODUCTION: Targeted Muscle Reinnervation (TMR) can prevent and treat neuropathic pain in amputees, but the degree of success varies. This study dev...

Multimodal fusion and explainability of artificial intelligence models in Alzheimer's Disease detection.

The integration of multimodal data has emerged as a powerful strategy for enhancing the accuracy and interpretability of artificial intelligence (AI) ...

Explainable multi-modal approach for uncovering key predictors of stroke-risk from ECG, EMG, blood pressure, and respiratory signals.

Accurate and timely stroke-risk prediction is necessary to help patients at risk take guided measures, as stroke remains a leading cause of death and ...