Publications by authors named "Mahsa Vatanparast"
Article Synopsis
- Traumatic brain injury (TBI) is a major global health issue, and this study aims to identify factors that predict changes in epidural hematoma (EDH) volume and develop machine learning models for prediction.
- Data from TBI patients with traumatic EDH between 2019-2021 was analyzed using various machine learning models divided into three groups: all cases, adults, and pediatrics.
- Key factors influencing EDH volume changes were identified, and the most effective predictive models were discovered, with Random Forest being the best for adults (82.4% AUROC), KNN for pediatrics (90%), and Logistic Regression for all cases (81.6%), potentially helping clinicians with timely patient interventions.
Objective: To evaluate the effectiveness and safety of flow-diverting stent (FDS) monotherapy in the treatment of intracranial blood blister-like aneurysms (BBAs) through a detailed systematic review.
Methods: This review adheres to the PRISMA guidelines, focusing on studies utilizing FDS monotherapy for BBAs, spanning from July 2010 to November 2023. A systematic search across databases including Embase, Medline/PubMed, Scopus, and Google Scholar was conducted.
Introduction: Traumatic intracranial aneurysms are rare, making up about 1% of all intracranial aneurysms. They can happen due to direct injury or blunt force, with the middle cerebral artery being the most frequent site. The middle meningeal artery (MMA) is the main artery that supplies the cranial dura mater, and, because of its location, is susceptible to damage after trauma.
View Article and Find Full Text PDFObjective: Surgeons commonly perform Decompressive craniectomy (DC) to manage patients with cerebral ischemic infarction. However, there are conflicting data on the long-term functional outcomes following DC. Therefore, this study aims to determine the functional outcome of patients with cerebral ischemic infarction after DC.
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