Are thinking machines breaking new frontiers in neuro-oncology? A narrative review on the emerging role of machine learning in neuro-oncological practice

Mustafa Hussain; Ainsia Shabbir; Saqib Bakhshi; Muhammad Shamim

doi:10.4103/ajns.AJNS_265_20

Asian Journal of Neurosurgery, Inhaltsverzeichnis

CC BY-NC-ND 4.0 · Asian J Neurosurg 2021; 16(01): 8-13
DOI: 10.4103/ajns.AJNS_265_20

Narrative Review Article

Are thinking machines breaking new frontiers in neuro-oncology? A narrative review on the emerging role of machine learning in neuro-oncological practice

Mustafa Hussain

¹Department of Neurosurgery, Aga Khan University Hospital, Karachi

,

Ainsia Shabbir

²Department of Computer and Information Systems Engineering, NED University of Engineering and Technology, Karachi

,

Saqib Bakhshi

¹Department of Neurosurgery, Aga Khan University Hospital, Karachi

,

Muhammad Shamim

¹Department of Neurosurgery, Aga Khan University Hospital, Karachi

› Institutsangaben

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.

Key-words:

Artificial intelligence - machine learning - neuro-oncology

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Referenzen

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