No abstract

Medical science in general and oncology in particular are dynamic, rapidly evolving subjects. Brain and spine tumors, whether primary or secondary, constitute a significant number of cases in any oncological practice. With the rapid influx of data in all aspects of neuro-oncological care, it is almost impossible for practicing clinicians to remain abreast with the current trends, or to synthesize the available data for it to be maximally beneficial for their patients. Machine-learning (ML) tools are fast gaining acceptance as an alternative to conventional reliance on online data. ML uses artificial intelligence to provide a computer algorithm-based information to clinicians. Different ML models have been proposed in the literature with a variable degree of precision and database requirements. ML can potentially solve the aforementioned problems for practicing clinicians by not just extracting and analyzing useful data, by minimizing or eliminating certain potential areas of human error, by creating patient-specific treatment plans, and also by predicting outcomes with reasonable accuracy. Current information on ML in neuro-oncology is scattered, and this literature review is an attempt to consolidate it and provide recent updates.

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Background: The coronavirus disease-19 pandemic has aggravated the already neglected neurosurgical specialty in developing countries with a mounting shortage of specialists, long queues of operative patients, and a lack of adequate critical care units. Methods: We have reviewed the innovative strategies adopted for maintaining an optimal surgical practice while ensuring team safety at the Aga Khan University Hospital, Karachi Pakistan. Results: There is already a scarcity of resources in developing countries. The international guidelines had to be tailored to the context of the developing world. A multimodal strategy that focused on infection control, continuum of care, and the well-being of staff was adopted at Aga Khan University. Patients were screened and seen either in person or through telemedicine, depending on the severity of the disease. All educational activities for residents were shifted online, and this helped in preventing overcrowding. Conclusion: Optimal surgical practice while ensuring team safety can be achieved through a multimodal strategy focusing on infection control, continuum of care, and the well-being of staff.