World Neurosurg
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Neurosurgery residency, known for its rigorous training, must adapt to evolving healthcare demands. Formal education should now encompass areas like quality improvement and patient safety, machine learning, career planning, research infrastructure, grant funding, and socioeconomics. We share our institution's experience with a yearlong enhanced didactics curriculum, complementing our traditional teaching. ⋯ Organized neurosurgery excels in clinical and technical training for residents but lacks formalized training in crucial nonclinical areas, such as quality improvement and patient safety, machine learning/artificial intelligence, research infrastructure, and socioeconomics. Our formal curriculum focused on these topics, with positive resident engagement and feedback over the first six months. However, continuous longitudinal monitoring is needed to confirm the curriculum's efficacy. This program may guide other neurosurgery departments in enhancing resident education in these areas.
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Artificial intelligence (AI) is increasingly significant in neurosurgery, enhancing differential diagnosis, preoperative evaluation, and surgical precision. A recent study in World Neurosurgery evaluated AI's role in aneurysm detection, comparing conventional computed tomography angiography images with AI analysis. AI identified 33 potential aneurysms, with 16 confirmed by radiologists, demonstrating a sensitivity of 36%, specificity of 97.6%, and a negative predictive value of 96.2%. ⋯ However, sensitivity for smaller aneurysms remained lower, indicating the need for further research. In conclusion, AI integration in aneurysm detection and management enhances diagnostic accuracy and precision. While current AI technologies show significant strengths, ongoing research is essential to address limitations and fully realize AI's potential in neurosurgery.
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Supportive radiologic signs may be needed to diagnose spondylolysis (SL) via lumbar magnetic resonance imaging (MRI). In SL, the slight displacement of the corpus forward and lamina posteriorly can cause the interposition of posterior epidural fat (EFI), which is normally segmental. This study aimed to determine the diagnostic value of EFI, an indirect sign of SL, on lumbar mid-sagittal T1-weighted MRI. ⋯ EFI is an indirect radiological finding with high reliability in diagnosing SL with mid-sagittal T1-weighted images in lumbar MRI.
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The skull base is a complex region in neurosurgery, featuring numerous foramina. Accurate identification of these foramina is imperative to avoid intraoperative complications and to facilitate educational progress in neurosurgical trainees. The intricate landscape of the skull base often challenges both clinicians and learners, necessitating innovative identification solutions. We aimed to develop a computer vision model that automates the identification and labeling of the skull base foramina from various image formats, enhancing surgical planning and educational outcomes. ⋯ This study successfully introduces a highly accurate computer vision model tailored for the identification of skull base foramina, illustrating the model's potential as a transformative tool in anatomical education and intraoperative structure visualization. The findings suggest promising avenues for future research into automated anatomical recognition models, suggesting a trajectory toward increasingly sophisticated aids in neurosurgical operations and education.