Anesthesiology and Clinical Science Research

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Abstract - Anesthesiology and Clinical Science Research (2021) Volume 5, Issue 2

A systematic review investigating the factors that affect the participation of children and adolescents in the vaccine research

Introduction: The disruption of normal patterns of structural brain connectivity is believed to play a central role in the pathophysiology of many neurological and psychiatric disorders, such as, dementia, movement disorders, stroke, traumatic brain injury (TBI) etc., Particularly, white matter changes lay in the heart of the onset of many pathologies. Traditional brain imaging technologies are expensive, inaccessible, and fail to provide actionable insights regarding brain network health. Therefore, there is a huge need, for a simple, precise and accessible tool that objectively evaluates brain functional status. Since the development of the X-ray in 1895, there have been many major advancements in medical imaging, and today the use of volumetric medical imaging is the backbone of 3D printing in medicine. Patient-specific 3D printed anatomic models may be created from any volumetric imaging dataset, with sufficient contrast and spatial resolution to separate structures, using dedicated image postprocessing software. The purpose of this chapter is to give a broad overview of the imaging systems that are typically used to create 3D printed anatomic models including computed tomography, magnetic resonance imaging, and ultrasound. In addition, imaging considerations for creating 3D printed anatomic models will be discussed. Brain tumor segmentation using Magnetic Resonance (MR) Imaging technology plays a significant role in computer-aided brain tumor diagnosis. However, when applying classic segmentation methods, limitations such as inhomogeneous intensity, complex physiological structure and blurred tissues boundaries in brain MR images usually lead to unsatisfactory results. DELPHITM is an active system for the visualization of brain health. It is a proprietary acquisition and analysis AI based algorithm that interfaces with available ?Off-the-Shelf? hardware to enable direct stimulation and monitoring of the brain (TMS-EEG). DELPHI?soutput measures, which areindicative for several electrophysiological features were significantly different between age defined groups as well as mild Dementia patients and age matched healthy controls. In a multidimensional approach the DELPHIoutput measures ability in identification of brain white matter fibres connectivity damage in stroke and traumatic brain injury (TBI) was tested. DELPHI output measures were able to classify healthy from unhealthy with a balanced accuracy of 0.81 Author(s): Lopa Banerjee

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