World Neurosurg
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Spinal cord injury (SCI) is a significant public health issue, leading to physical, psychological, and social complications. Machine learning (ML) algorithms have shown potential in diagnosing and predicting the functional and neurologic outcomes of subjects with SCI. ML algorithms can predict scores for SCI classification systems and accurately predict outcomes by analyzing large amounts of data. This systematic review aimed to examine the performance of ML algorithms for diagnosing and predicting the outcomes of subjects with SCI. ⋯ ML and deep learning (DL) have shown great potential in various aspects of SCI. ML and DL algorithms have been employed multiple times in predicting and diagnosing patients with SCI. While there are studies on diagnosing acute SCI using DL algorithms, further research is required in this area.
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The balance between comprehensive intraoperative neurophysiological monitoring (IONM) for both upper and lower limbs while ensuring the reliability of motor evoked potentials (MEPs) is paramount in motor area surgery. It is commonly difficult to obtain good simultaneous stimulation of both upper and lower limbs. A series of factors can bias MEP accuracy, and inappropriate stimulation intensity can result in unreliable monitoring. The presented IONM technique is based on the concurrent use of both transcranial and cortical strip electrodes to facilitate simultaneous monitoring of both upper and lower limbs at optimized stimulation intensities to increase IONM accuracy during motor area surgery. ⋯ The technique was demonstrated to be accurate in providing a good simultaneous neurophysiological evaluation of both upper and lower limbs with an optimized and stimulation amplitude. The technique results in a low encumbrance of electrodes in the surgical field. Our results have confirmed the "proof of concept," its reliability and feasibility.
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To evaluate variability in aneurysm detection and the potential of artificial intelligence (AI) software as a screening tool by comparing conventional computed tomography angiography (CTA) images (standard care) with AI software. ⋯ AI has the potential to increase the diagnosis of unruptured intracranial aneurysms. However, it must be used as an adjacent tool within the standard of care due to limited applicability in real-world settings.
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To describe a simple variation of burr hole craniostomy for the management of chronic subdural hematoma (CSDH) that uses a frontal drainage system to facilitate timely decompression in the event of tension pneumocephalus and spares the need for additional surgery. ⋯ The described variation of burr hole craniostomy represents a low-cost and easy-to-implement technique that can be used for emergency decompression of tension pneumocephalus. It also has the potential to reduce reoperation rates and CSDH recurrence. Prospective controlled research is needed to validate this approach further.
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Endoscopic procedures are useful in chronic subdural hematoma especially when there are septations, solid/organized hematoma, and the presence of bridging or neovessels in the cavity. Visualizing the distal hematoma cavity by a rigid scope is challenging in large and curved ones due to the hindrance by the brain surface. Combining rigid endoscopy and brain retractor can overcome this limitation. ⋯ Endoscope was very effective and safe in the management of chronic subdural hematoma, especially in about 51% patients with solid clots, septations, and bridging vessels which could have been difficult to treat by conventional burr hole. It can avoid craniotomy in such patients. Good visualization and complete hematoma removal were possible with the help of an endoscope and brain retractor in about 27% of patients which could have been difficult with a rigid endoscope alone.