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    Neurosurgery with Artificial Intelligence

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    The safety and efficacy of surgery are the surgeon’s priority. With the help of artificial intelligence (AI) tools, with learning algorithms based on millions of patients’ records, the outcomes can be predicted following different types of surgeries. 1 According to the Journal of the American Medical Association by the Neurosurgical Simulation and Artificial Intelligence Learning Centre at The Neuro, McGill University, AI can enhance technical performance and learning outcomes during simulated brain tumor removal. 2 According to a study published in Nature partner journal Digital Medicine, an open-source smartphone app for meningioma has allowed surgeons and physicians to interactively with patients. The app helps doctors and brain tumor patients predict survival and help make better treatment decisions. Researchers have also created a machine-learning system to categorize tissue using a deep convolutional neural network (CNN).3 New research also showed that machine learning can diagnose brain tumors at a fraction of the time it takes human pathologists. The new method streamlined the practice of analyzing tissue samples while the patient is still on the operating table. As per new findings in Nature Medicine, the new AI system can accurately diagnose a brain tumor in two minutes4. We should place a high value on research based on anticipating neuropathologies, surgical outcomes, and choosing neurosurgical techniques in advance

    Computational biomarkers for the decision of neurosurgical treatment for brain tumors

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    Computational biomarkers are a promising area of research for the decision of neurosurgical treatment for brain tumors. These biomarkers can help to predict the biological behavior of tumors, such as their growth rate, invasiveness, and response to treatment. Computational biomarkers can play an important role in the decision-making process for the neurosurgical treatment of brain tumors. These biomarkers provide quantitative and objective measures of tumor characteristics that can help inform the choice of treatment and predict patient outcomes
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