Overview of Cerebrospinal Fluid, Glymphatic System, and Diagnosis of Multiple Sclerosis Through Machine Learning Extrapolation
DOI:
https://doi.org/10.58445/rars.528Keywords:
glymphatic system, cerebrospinal fluid, machine learning, artificial intelligence, multiple sclerosis, biomarkersAbstract
The glymphatic system is a newly discovered process in the subarachnoid space that regulates metabolic clearance in the brain. It will revolutionize the scientific understanding of sleep, cognitive function, and CNS disorders. In this article, the evolutionary origins and roles of cerebrospinal fluid will be analyzed to learn more about this clearance system. Moreover, this article will review and discuss the entirety of the glymphatic system, specifically the processes that occur in the perivascular space. Certain glial cells and proteins will be highlighted, such as astrocytes and AQP4, respectively, to further explain fluid transport and entry into the lymphatic network. The purpose of sleep and the accumulation of harmful CSF by-products will be closely scrutinized in relation to glymphatic impairment. CNS disorders have recently been associated with these neurobiological problems. Current research provides the use of CSF biomarker information to diagnose and identify neurodegenerative diseases. The new rise in deep learning, artificial neural networks, has innovated the possibilities of machine learning. Artificial intelligence algorithms, such as Random Forest, will be utilized to extrapolate data on prevalent neurobiological factors of multiple sclerosis (MS) patients. The program will also demonstrate machine learning capabilities by predicting MS diagnosis in both males and females. The new age of neurotechnology will feature more tangible and limitless roles in biomarker identification of CNS disorders. The aim of this article is to explore the glymphatic system and cerebrospinal fluid with future technological innovations.
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