Latest AI and machine learning research in neurology for healthcare professionals.
INTRODUCTION: This study presents a novel multi-view learning approach for machine learning (ML)-based Alzheimer's disease (AD) diagnosis.
BACKGROUND: Interest is emerging regarding the role of blood biomarkers in acute stroke. The aim of this pilot study was to determine the feasibility of biomarker acquisition in suspected acute stroke, using modern ultrasensitive immunoassay techniques, and explore their potential usefulness for stroke diagnosis and management.
AD is a progressive neurodegenerative disorder characterized by memory loss. Due to the advancement in next-generation sequencing, an enormous amount ...
The integration of machine learning (ML) classification techniques into migraine research has offered new insights into the pathophysiology and classi...
BACKGROUND: Impaired balance and gait in stroke survivors are associated with decreased functional independence. This study aimed to evaluate the effe...
Millions of people around the globe are impacted by falls annually, making it a significant public health concern. Falls are particularly challenging ...
This study investigates the effectiveness of amplitude transformation in enhancing the performance and robustness of Multiscale Fuzzy Entropy for Alzh...
Detecting gait abnormalities is crucial for assessing fall risks and early identification of neuromusculoskeletal disorders such as Parkinson's and st...
Sleep staging is essential for sleep assessment and plays an important role in disease diagnosis, which refers to the classification of sleep epochs i...
OBJECT: Transformer-based neural networks have been applied to the electroencephalography (EEG) decoding for motor imagery (MI). However, most network...
Self-supervised learning (SSL) is a challenging task in sleep stage classification (SSC) that is capable of mining valuable representations from unlab...
Cerebrovascular segmentation is a crucial preliminary task for many computer-aided diagnosis tools dealing with cerebrovascular pathologies. Over the ...
Robot-assisted gait training (RAGT) is a promising technique for improving the gait ability of elderly adults and patients with gait disorders by enab...
BACKGROUND: This study focused on the research hotspots and development trends of the neuroimaging of social anxiety (SA) in the past 25 years.
EEG signal analysis can be used to study brain activity and the function and structure of neural networks, helping to understand neural mechanisms suc...
INTRODUCTION: In Parkinson's Disease (PD), despite available treatments focusing on symptom alleviation, the effectiveness of conventional therapies d...
In this study, we investigate the application of self-supervised learning via pre-trained Long Short-Term Memory (LSTM) networks for training surface ...
PURPOSE: This study aims to evaluate the similarity, readability, and alignment with current scientific knowledge of responses from AI-based chatbots ...
Flexible strain monitoring of hand and joint muscle movement is recognized as an effective method for the diagnosis and rehabilitation of neurological...
In the domain of passive brain-computer interface applications, the identification of emotions is both essential and formidable. Significant research ...