Journal of neurointerventional surgery
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Review
Minimizing SARS-CoV-2 exposure when performing surgical interventions during the COVID-19 pandemic.
Infection from the SARS-CoV-2 virus has led to the COVID-19 pandemic. Given the large number of patients affected, healthcare personnel and facility resources are stretched to the limit; however, the need for urgent and emergent neurosurgical care continues. This article describes best practices when performing neurosurgical procedures on patients with COVID-19 based on multi-institutional experiences. ⋯ Based on a multi-institutional collaborative effort, we describe best practices when providing neurosurgical treatment for patients with COVID-19 in order to optimize clinical care and minimize the exposure of patients and staff.
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Acute stroke caused by large vessel occlusions (LVOs) requires emergent detection and treatment by endovascular thrombectomy. However, radiologic LVO detection and treatment is subject to variable delays and human expertise, resulting in morbidity. Imaging software using artificial intelligence (AI) and machine learning (ML), a branch of AI, may improve rapid frontline detection of LVO strokes. This report is a systematic review of AI in acute LVO stroke identification and triage, and characterizes LVO detection software. ⋯ AI may improve LVO stroke detection and rapid triage necessary for expedited treatment. Standardization of performance assessment is needed in future studies.
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With the benefit of mechanical thrombectomy firmly established, the focus has shifted to improved delivery of care. Reducing time from symptom onset to reperfusion is a primary goal. Technology promises tremendous opportunities in the prehospital space to achieve this goal. ⋯ The major challenges to tackle for future improvement in prehospital stroke care are that of public awareness, emergency medical service detection, and triage, and improved systems of stroke care. Thoughtfully applied technology will transform all these areas.