Latest AI and machine learning research in neurology for healthcare professionals.
In recent years, machine learning and deep learning have shown potential for improving Parkinson's disease (PD) diagnosis, one of the most common neurodegenerative diseases. This comprehensive analysis examines machine learning and deep learning-based Parkinson's disease diagnosis using MRI, speech, and handwriting datasets. To thoroughly analyze PD, this study collected data from scientific liter...
As the older adult population continues to expand, the demands on the healthcare system intensifies, necessitating the development of technologies that effectively accommodate the requirements of older adults. While Artificial Intelligence (AI) systems hold promise as a solution, they have not been designed to accommodate the sensory and cognitive changes typical of aging individuals. The current ...
Virtual reality (VR), particularly through 3D presentations, significantly boosts user engagement and task efficiency in fields such as gaming, educat...
Electroencephalography (EEG) microstate analysis is a promising technique for detecting transient brain dynamics and identifying disease-specific biom...
OBJECTIVE: Post-stroke depression (PSD) is a common psychiatric complication following stroke, with low clinical detection rates and delayed diagnosis...
One of the main risk factors for numerous health problems is excessive drinking. Alcoholism is a severe disorder that can affect a person's thinking a...
Alzheimer's disease (AD) is a neurodegenerative disorder characterized by a progressive cognitive decline, necessitating early diagnosis for effective...
Parkinson's disease (PD) is a neurodegenerative disorder that occurs as a result of a decrease in the chemical called dopamine in the brain. There is ...
Although there are notable structural abnormalities in the brain associated with psychotic diseases, it is still unclear how these abnormalities relat...
Central Apnea (CA) and Central Hypopnea (CH) are sleep disorders arising from the brain's inability to signal respiratory muscles, potentially leading...
In this paper, we present a systematic literature review on deep generative models for physiological signals, particularly electrocardiogram (ECG), el...
Deep learning for Electroencephalography (EEG) has become dominant in the tasks of discrimination and evaluation of brain disorders. However, despite ...
BACKGROUND: Diabetes mellitus has been shown to increase the risk of dementia, with diabetic patients demonstrating twice the dementia incidence rate ...
BACKGROUND: The immune microenvironment significantly influences neural regeneration in spinal cord injury (SCI). Lactate activates central nervous sy...
Previous resting state functional MRI (rs-fMRI) analyses of the basal ganglia in Parkinson's disease heavily relied on T1-weighted imaging (T1WI) atla...
Patients with amyotrophic lateral sclerosis (ALS) in the completely locked-in state (CLIS) can lose all reliable motor control and are left without ...
Deep learning (DL) methods are increasingly outperforming classical approaches in brain imaging, yet their generalizability across diverse imaging c...
Understanding and decoding brain activity from electroencephalography (EEG) signals is a fundamental challenge in neuroscience and AI, with applicat...
The global prevalence of dementia is projected to double by 2050, highlighting the urgent need for scalable diagnostic tools. This study utilizes di...
Adapting machine learning models to medical time series across different domains remains a challenge due to complex temporal dependencies and dynami...