Latest AI and machine learning research in neurology for healthcare professionals.
As a modal of physiological information, electroencephalogram (EEG), surface electromyography (sEMG), and eye tracking (ET) signals are widely used to decode human intention, promoting the development of human-computer interaction systems. Extensive studies have achieved single-modal signal decoding with substantial structural differences but consuming mass computing resources and development cost...
PURPOSE OF REVIEW: To summarize recent advancements in artificial intelligence-driven lesion segmentation and novel neuroimaging modalities that enhance the identification and characterization of multiple sclerosis (MS) lesions, emphasizing their implications for clinical use and research.
OBJECTIVE: Cortical regions such as parietal area H (PH) and the fundus of the superior temporal sulcus (FST) are involved in higher visual function a...
Stroke is a leading global cause of death, with 80% of cases considered preventable through early detection and awareness. This study employs artifici...
Parasomnias are abnormal behaviours or mental experiences during sleep or the sleep-wake transition. As disorders of arousal (DOA) or REM sleep behavi...
BACKGROUND: Regarding the differences in surgical approaches for spinal schwannomas and meningiomas, preoperative differentiation of spinal schwannoma...
Alzheimer's disease (AD), the most common form of dementia, is marked by cognitive decline and amyloid-beta (Aβ) plaque deposition in the brain. Micro...
Yu 's study has advanced the understanding of the neural mechanisms underlying major depressive disorder (MDD) in adolescents, emphasizing the signifi...
Diagnosing Alzheimer's disease based on gene expression data extracted from microarrays is still an open field of research. Due to the availability of...
Lie detection is crucial in domains such as security, law enforcement, and clinical assessments. Traditional methods suffer from reliability issues a...
Driven by the remarkable capabilities of machine learning, brain-computer interfaces (BCIs) are carving out an ever-expanding range of applications ac...
This study aimed to classify patients' focal (frontal, temporal, parietal, occipital), multifocal, and generalized epileptiform activities based on EE...
BACKGROUND AND OBJECTIVE: Prolonged abnormal emotions can gradually evolve into mood disorders such as anxiety and depression, making it critical to s...
In treating malignant cerebral edema after a large middle cerebral artery stroke, clinicians need quantitative tools for real-time risk assessment. Ex...
Processed electroencephalography (EEG) indices used to guide anesthetic dosing in adults are not validated in young infants. Raw EEG can be processed ...
The recent approval of anti-amyloid pharmaceuticals for the treatment of Alzheimer's disease (AD) has created a pressing need for the ability to accur...
OBJECTIVE: To develop and validate machine learning-based models for predicting independent walking ability in children with cerebral palsy (CP) DESIG...
BACKGROUND: Human immunodeficiency virus (HIV), while now manageable as a chronic health condition with highly active antiretroviral therapy (HAART), ...
Early and precise diagnosis of dyslexia is crucial for implementing timely intervention to reduce its effects. Timely identification can improve the i...
Carpal Tunnel Syndrome (CTS) is the most common entrapment neuropathy, frequently presenting with pain, numbness, and muscle weakness due to median ne...