Bibliographic Details
| Title: |
Diagnostic and therapeutic approach of artificial intelligence in neuro-oncological diseases |
| Authors: |
Dhivya Venkatesan, Ajay Elangovan, Harysh Winster, Md Younus Pasha, Kripa Susan Abraham, Satheeshkumar J, Sivaprakash P, Ayyadurai Niraikulam, Abilash Valsala Gopalakrishnan, Arul Narayanasamy, Balachandar Vellingiri |
| Source: |
Biosensors and Bioelectronics: X, Vol 11, Iss , Pp 100188- (2022) |
| Publisher Information: |
Elsevier, 2022. |
| Publication Year: |
2022 |
| Collection: |
LCC:Biotechnology |
| Subject Terms: |
Neuro-oncological disease, Neurology, Artificial intelligence (AI), Imaging techniques, Machine learning, Deep learning, Biotechnology, TP248.13-248.65 |
| More Details: |
Neuro-oncological diseases are rare and their fatality rate is increased in patients due to advance disease development despite of the recent outcomes on neuro-oncological therapies. Artificial intelligence (AI) approaches and the exponential expansion of computing algorithms are set to increase the precision of diagnostic and therapeutic approaches in medicine. Medical imaging is one of the common AI applications where it assists radiologists in diagnosis. Radiomics has been successfully applied in neuro-oncology and it will be at forefront of AI revolution. Various AI methods can define numerous infiltrating margins of neuro-oncological diseases and it differentiates pseudo-progression from real progression and envisage recurrence and survival better than the methods used in routine practice. The present review deliberates the common neuro-oncological diseases such as glioblastoma, meningioma, spinal cord tumor and neurofibroma (NF1) and its AI algorithms related to imaging techniques such as computed (MRI) and computed tomography (CT). Also, we have discussed the beneficial aspect of AI and recent trends in diagnosis. From the study, the management of neuro-oncological diseases using AI can be revolutionized and the need of omics analysis is essential in future. |
| Document Type: |
article |
| File Description: |
electronic resource |
| Language: |
English |
| ISSN: |
2590-1370 |
| Relation: |
http://www.sciencedirect.com/science/article/pii/S2590137022000826; https://doaj.org/toc/2590-1370 |
| DOI: |
10.1016/j.biosx.2022.100188 |
| Access URL: |
https://doaj.org/article/cc7656b0a39743058db667a4c2c490cd |
| Accession Number: |
edsdoj.7656b0a39743058db667a4c2c490cd |
| Database: |
Directory of Open Access Journals |
