Neurology

Shared autonomy between human electroencephalography and TD3 deep reinforcement learning: A multi-agent copilot approach.

Deep reinforcement learning (RL) algorithms enable the development of fully autonomous agents that can interact with the environment. Brain-computer interface (BCI) systems decipher human implicit brain signals regardless of the explicit environment. We proposed a novel integration technique between deep RL and BCI to improve beneficial human interventions in autonomous systems and the performance...

Efficient Seizure Detection by Complementary Integration of Convolutional Neural Network and Vision Transformer.

Epilepsy, as a prevalent neurological disorder, is characterized by its high incidence, sudden onset, and recurrent nature. The development of an accurate and real-time automatic seizure detection system is crucial for assisting clinicians in making precise diagnoses and providing timely treatment for epilepsy. However, conventional automatic seizure detection methods often face limitations in sim...

Advanced convolutional neural network with attention mechanism for Alzheimer's disease classification using MRI.

This paper introduces a novel convolutional neural network model with an attention mechanism to advance Alzheimer disease (AD) classification using Ma...

Handwriting strokes as biomarkers for Alzheimer's disease prediction: A novel machine learning approach.

In recent years, machine learning-based handwriting analysis has emerged as a valuable tool for supporting the early diagnosis of Alzheimer's disease ...

Artificial intelligence applied to electroencephalography in epilepsy.

Artificial intelligence (AI) is progressively transforming all fields of medicine, promising substantial changes in clinical practice. In the context ...

Enhancing convolutional neural networks in electroencephalogram driver drowsiness detection using human inspired optimizers.

Driver drowsiness is a significant safety concern, contributing to numerous traffic accidents. To address this issue, researchers have explored electr...

Flexible Patched Brain Transformer model for EEG decoding.

Decoding the human brain using non-invasive methods is a significant challenge. This study aims to enhance electroencephalography (EEG) decoding by de...

The third wheel or the game changer? How AI could team up with neurologists in Parkinson's care.

INTRODUCTION: Parkinson's disease (PD) is a progressive neurodegenerative disorder marked by diverse motor and non-motor symptoms. AI appears to be th...

Extract optimization and biological activities of Otidea onotica using Artificial Neural Network-Genetic Algorithm and response surface methodology techniques.

In this study, the biological activities of Otidea onotica were investigated using two optimization methods, Response Surface Methodology (RSM) and Ar...

Comparative Assessment of Manual Segmentation of Cerebral Infarction Lesions in Experimental Animals Based on Magnetic Resonance Imaging Using Artificial Intelligence.

The aim of this study was to evaluate the quality of manual segmentation of cerebral infarction lesions in experimental animals with modeled brain inf...

Electroencephalography Decoding with Conditional Identification Generator.

Decoding Electroencephalography (EEG) signals are extremely useful for advancing and understanding human-artificial intelligence (AI) interaction syst...

External validation of a machine learning prediction model for massive blood loss during surgery for spinal metastases: a multi-institutional study using 880 patients.

BACKGROUND CONTEXT: A machine learning (ML) model was recently developed to predict massive intraoperative blood loss (>2,500 mL) during posterior dec...

The NERVE-ML (neural engineering reproducibility and validity essentials for machine learning) checklist: ensuring machine learning advances neural engineering.

Machine learning's (MLs) ability to capture intricate patterns makes it vital in neural engineering research. With its increasing use, ensuring the va...

An Assessment of the Performance of Different Chatbots on Shoulder and Elbow Questions.

The utility of artificial intelligence (AI) in medical education has recently garnered significant interest, with several studies exploring its appli...

Future horizons in diabetes: integrating AI and personalized care.

Diabetes is a global health crisis with rising incidence, mortality, and economic burden. Traditional markers like HbA1c are insufficient for capturin...

Binding interaction of acetylcholinesterase with steroidal pregnanes: insight from machine learning and atomistic simulation.

Acetylcholinesterase (AChE) inhibition is a key strategy in the treatment of Alzheimer's disease and other neurodegenerative disorders. While pregnane...

Predicting quality of life of patients after treatment for spinal metastatic disease: development and internal evaluation.

BACKGROUND CONTEXT: When treating spinal metastases in a palliative setting, maintaining or enhancing quality of life (QoL) is the primary therapeutic...

Physiological Sensor Modality Sensitivity Test for Pain Intensity Classification in Quantitative Sensory Testing.

Chronic pain is prevalent and disproportionately impacts adults with a lower quality of life. Although subjective self-reporting is the "gold standard...

Role of tunneling nanotubes in neuroglia.

Tunneling nanotubes are crucial structures for cellular communication and are observed in a variety of cell types. Glial cells, the most abundant cell...

Traumatic brain injury: Bridging pathophysiological insights and precision treatment strategies.

Blood-brain barrier disruption and the neuroinflammatory response are significant pathological features that critically influence disease progression ...