Objective: To radiologically examine the pedicle, lamina, and vertebral artery foraminal anatomies at the C2 vertebra for pedicular and laminar screw instrumentation at the axis in a Turkish population.
Methods: From 2018 to 2019, we evaluated 100 patients who underwent cervical computed tomography (CT) for various reasons (excluding cervical pathologies) at Marmara University Hospital. The C2 pedicles were measured on CT images using measurement tools.
Background: This study aims to assess the quality and reliability of the information for patients from YouTube videos on transforaminal interbody fusion (TLIF).
Material And Methods: One hundred videos were listed by inputting "TLIF," "TLIF surgery," and "transforaminal interbody fusion" in the YouTube search engine. The top 50 most popular videos based on video power index (VPI), view ratio, and exclusion criteria were selected for review.
Objective: The primary aim of this research was to harness the capabilities of deep learning to enhance neurosurgical procedures, focusing on accurate tumor boundary delineation and classification. Through advanced diagnostic tools, we aimed to offer surgeons a more insightful perspective during surgeries, improving surgical outcomes and patient care.
Methods: The study deployed the Mask R-convolutional neural network (CNN) architecture, leveraging its sophisticated features to process and analyze data from surgical microscope videos and preoperative magnetic resonance images.