Use of artificial intelligence and radio genomics in neuroradiology and the future of brain tumour imaging and surgical planning in low- and middleincome countries

Abstract

Brain tumour diagnosis involves assessing various
radiological and histopathological parameters. Imaging
modalities are an excellent resource for disease
monitoring. However, manual inspection of imaging is
laborious, and performance varies depending on
expertise. Artificial Intelligence (AI) driven solutions a
non-invasive and low-cost technology for diagnostics
compared to surgical biopsy and histopathological
diagnosis. We analysed various machine learning models
reported in the literature and assess its applicability to
improve neuro-oncological management. A scoping
review of 47 full texts published in the last 3 years
pertaining to the use of machine learning for the
management of different types of gliomas where
radiomics and radio genomic models have proven to be
useful. Use of AI in conjunction with other factors can
result in improving overall neurooncological
management within LMICs. AI algorithms can evaluate
medical imaging to aid in the early detection and
diagnosis of brain tumours. This is especially useful where
AI can deliver reliable and efficient screening methods,
allowing for early intervention and treatment.
Keywords: Artificial Intelligence, Radiomics, Machine
Learning, Genomics, Brain Neoplasms, Glioma, Biopsy