An Official publication of The Asian Congress of Neurological Surgeons (AsianCNS)

Search Article
Home About us Editorial board Search Ahead of print Current issue Archives Submit article Instructions Advertise Subscribe Contacts Login  Facebook Tweeter
  Users Online: 106 Home Print this page Email this page Small font sizeDefault font sizeIncrease font size  
NARRATIVE REVIEW ARTICLE
Year : 2021  |  Volume : 16  |  Issue : 1  |  Page : 8-13

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


1 Department of Neurosurgery, Aga Khan University Hospital, Karachi, Pakistan
2 Department of Computer and Information Systems Engineering, NED University of Engineering and Technology, Karachi, Pakistan

Correspondence Address:
Dr. Muhammad Shahzad Shamim
Aga Khan University Hospital, Stadium Road, P.O. Box 3500, Karachi 74800
Pakistan
Login to access the Email id

Source of Support: None, Conflict of Interest: None


DOI: 10.4103/ajns.AJNS_265_20

Rights and Permissions

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.


[FULL TEXT] [PDF]*
Print this article     Email this article
 Next article
 Previous article
 Table of Contents

 Similar in PUBMED
   Search Pubmed for
   Search in Google Scholar for
 Related articles
 Citation Manager
 Access Statistics
 Reader Comments
 Email Alert *
 Add to My List *
 * Requires registration (Free)
 

 Article Access Statistics
    Viewed288    
    Printed17    
    Emailed0    
    PDF Downloaded47    
    Comments [Add]    

Recommend this journal