World Neurosurg
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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. ⋯ As illustrated in our study, the confluence of deep learning with neurosurgical procedures marks a transformative phase in medical science. The results are promising but underscore diverse data sets' significance for training and refining these deep learning models.
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Meningiomas display diverse biological traits and clinical behaviors, complicating patient outcome prediction. This heterogeneity, along with varying prognoses, underscores the need for a precise, personalized evaluation of postoperative outcomes. ⋯ The study successfully demonstrated the potential of machine learning models in predicting short-term adverse postoperative outcomes after meningioma resections. This approach represents a significant step forward in personalizing the information provided to meningioma patients.
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Intraoperative ultrasound (IOUS) images can be distorted by various artifacts. During surgeries for insular low-grade gliomas (LGGs), we repeatedly observed a distinct hyperechoic artifact adjacent to medial tumor borders, localized in brain regions with normal appearance on magnetic resonance imaging (MRI) that has not been reported before. ⋯ Although the causes of this bright artifact are unclear, we can hypothesize that the reverberation in between different parallel layers of white and gray matter localized under the insula could play a role in its appearance. Importantly, as this hyperechoic area was depicted already before any tumor resection, it may lead to erroneous conclusion that the tumor spreads more medially. Potential resection in this region may cause significant neurologic sequelae.
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Multicompartmental lesions of the anterior craniovertebral junction require aggressive management. However, the lesions can be difficult to reach, and the surgical procedure is difficult to understand. The aim of this study was to create a procedural, stepwise microsurgical educational resource for junior trainees to learn the surgical anatomy of the extreme lateral transodontoid approach (ELTOA). ⋯ The ELTOA is a challenging approach, but it allows for significant access to the anterior craniovertebral junction, which increases the likelihood of gross total lesion resection. Given the complexity of the approach, substantial training in the dissection laboratory is required to develop the necessary anatomic knowledge and to minimize approach-related morbidity.
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Augmented reality (AR) is an emerging technology in neurosurgery with the potential to become a strategic tool in the delivery of care and education for trainees. Advances in technology have demonstrated promising use for improving visualization and spatial awareness of critical neuroanatomic structures. In this report, we employ a novel AR registration system for the visualization and targeting of skull landmarks. ⋯ While several areas of improvement and innovation can further enhance the use of AR in neurosurgery, this report demonstrates the feasibility of a markerless headset-based AR system for visualizing craniometric points on the skull. As the technology continues to advance, AR is expected to play an increasingly significant role in neurosurgery, transforming how surgeries are performed and improving patient care.