World Neurosurg
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Randomized Controlled Trial Comparative Study
Comparison of The Efficacy of Sterile Silicone Studs vs Lidocaine for the Attenuation of the Hemodynamic Response to Skull Pin Insertion: A Randomized Controlled Trial: The role of sterile silicone studs and scalp infiltration with lignocaine in the attenuation of the hemodynamic response to skull pin insertion.
Skull pin insertion causes hypertension and tachycardia that adversely affects cerebral hemodynamics. We compared the efficacy of sterile silicone studs (SS) and pin site infiltration with lidocaine in attenuation of the sympathetic response to skull pin insertion. ⋯ Sterile SS appear to be more effective than lidocaine infiltration in attenuating the hemodynamic response to skull pin insertion with minimal adverse effects. Further multicenter studies are necessary to conclusively establish the safety and efficacy of sterile SS.
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High-fidelity visualization of anatomical organs is crucial for neurosurgical education, simulation, and planning. This becomes much more important for minimally invasive neurosurgical procedures. Realistic anatomical visualization can allow resident surgeons to learn visual cues and orient themselves with the complex 3-dimensional (3D) anatomy. Achieving full fidelity in 3D medical visualization is an active area of research; however, the prior reviews focus on the application area and lack the underlying technical principles. Accordingly, the present study attempts to bridge this gap by providing a narrative review of the techniques used for 3D visualization. ⋯ The visualization of virtual human organs has not yet achieved a level of realism close to reality. This gap is largely due to the interdisciplinary nature of this research, population diversity, and validation complexities. With the advancements in computational resources and automation of 3D visualization pipelines, next-gen applications may offer enhanced medical 3D visualization fidelity.
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Traumatic brain injury (TBI) is a highly prevalent and potentially severe medical condition. Challenges regarding TBI management are related to accurate diagnostics, defining its severity, and establishing prompt interventions to affect outcomes. Among the health care components in the TBI handling strategy is intracranial pressure (ICP) monitoring, which is fundamental to therapy decisions. However, ICP monitoring is an Achilles tendon, imposing a significant financial burden on health care systems, particularly in middle and low-income communities. This article arises from the understanding from the authors that there is insufficient scientific evidence about the potential economic impacts from the use of noninvasive technologies in the monitoring of TBI. Based on personal experience, as well as from reading other, clinically focused studies, the thesis is that the use of such technologies could greatly affect the health care system and this article seeks to address this lack of literature, show ways in which such systems could be evaluated, and show estimations of possible results from these investigations. ⋯ TBI prevalence has increased with a disproportionate health care burden in the last decades. Noninvasive monitoring techniques seem to be effective in reducing TBI health care costs, with few limitations, especially the need for more supporting scientific evidence. The undeniable clinical and financial potential of these techniques is compelling to further investigate their role in TBI management, as well as the creation of more comprehensive monitoring models to the understanding of complex phenomena occurring in the injured brain.
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Keyhole surgery has been widely used to clip various intracranial aneurysms. Here, the feasibility of microsurgical clipping of multiple intracranial aneurysms via the keyhole approach was further investigated. ⋯ Early keyhole surgical clipping of multiple intracranial aneurysms is an effective treatment. Among ruptured aneurysms, small aneurysms are common and need attention and timely treatment.
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Machine learning and deep learning techniques offer a promising multidisciplinary solution for subarachnoid hemorrhage (SAH) detection. The novel transfer learning approach mitigates the time constraints associated with the traditional techniques and demonstrates a superior performance. This study aims to evaluate the effectiveness of convolutional neural networks (CNNs) and CNN-based transfer learning models in differentiating between aneurysmal SAH and nonaneurysmal SAH. ⋯ CNN-based transfer learning models can accurately diagnose the etiology of SAH from computed tomography images and is a valuable tool for clinicians. This approach could reduce the need for invasive procedures such as digital subtraction angiography, leading to more efficient medical resource utilization and improved patient outcomes.