Neurosurgery
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Machine learning (ML) is a domain of artificial intelligence that allows computer algorithms to learn from experience without being explicitly programmed. ⋯ We conclude that ML models have the potential to augment the decision-making capacity of clinicians in neurosurgical applications; however, significant hurdles remain associated with creating, validating, and deploying ML models in the clinical setting. Shifting from the preconceptions of a human-vs-machine to a human-and-machine paradigm could be essential to overcome these hurdles.
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The Manchester criteria for neurofibromatosis type 2 (NF2) include a range of tumors, and gliomas were incorporated in the original description. The gliomas are now widely accepted to be predominantly spinal cord ependymomas. ⋯ High-grade gliomas are not a feature of NF2 in the unirradiated patient and should be excluded from the diagnostic criteria.
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Resective surgery established treatment for pharmacoresistant frontal lobe epilepsy (FLE), but seizure outcome and prognostic indicators are poorly characterized and vary between studies. ⋯ Surgical resection in drug-resistant FLE can be a successful therapeutic approach, even in the absence of neuroradiologically visible lesions. SEEG may be highly useful in both nonlesional and lesional FLE cases, because complete resection of the EZ as defined by SEEG is associated with better prognosis.
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Image guidance based on magnetic resonance imaging (MRI) and/or ultrasound (US) is widely used to aid decision making in glioma surgery, but tumor delineation based on these 2 modalities does not always correspond. ⋯ The tumor volumes of LGGs segmented from intraoperative US images were most often smaller than the tumor volumes segmented from preoperative MRIs. There was a much better match between the 2 modalities in astrocytomas.