Artificial intelligence use in diagnosis, grading, and segmentation of neuro-oncology: a narrative review
Abstract Background Artificial intelligence (AI) is a broad term that encompasses Machine Learning (ML) and Deep Learning (DL). Advancements in AI and its methodologies allow its application in multiple stages of neuro-oncology management. This article aims to provide a comprehensive review of curre...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2025-06-01
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| Series: | The Egyptian Journal of Neurology, Psychiatry and Neurosurgery |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s41983-025-00980-7 |
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| Summary: | Abstract Background Artificial intelligence (AI) is a broad term that encompasses Machine Learning (ML) and Deep Learning (DL). Advancements in AI and its methodologies allow its application in multiple stages of neuro-oncology management. This article aims to provide a comprehensive review of current applications of AI in neuro-oncology diagnosis, segmentation, and grading. In addition, it expresses the challenges faced in those fields. This is the only study that includes those three fields of AI use in neuro-oncology. The search in four databases (Scopus, PubMed, Wiley, and Google Scholar) gave a total of 28 articles using AI in neuro-oncology diagnosis, segmentation, and grading. Articles were collected and reviewed, and data were summarized and presented in tables. Results The majority of the articles are about diagnosis 13, with the radiological diagnosis as the most used method by AI, followed by Segmentation in 10 articles, and then grading 5. Chattopadhyay's study, which diagnoses using MRI and CNN-based deep learning methods, has the highest sample size, 2473, among all included articles. It also has the highest accuracy among others, reaching 99.74%. Conclusions AI in neuro-oncology is promising and rapidly growing despite the challenges. We still need more research applying AI in brain tumors to aid in developing new therapeutic targets and a better understanding of brain tumors. |
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| ISSN: | 1687-8329 |