| Autor: |
Wijethilake, Navodini, Connor, Steve, Oviedova, Anna, Kujawa, Aaron, Burger, Rebecca, Vercauteren, Tom, Shapey, Jonathan |
| Předmět: |
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| Zdroj: |
Journal of Neurological Surgery. Part B. Skull Base; 2024 Supplement, Vol. 85, pS1-S398, 398p |
| Abstrakt: |
This article discusses the development of an automated clinical feature extraction framework for vestibular schwannoma (VS), the most common brain tumor in the cerebellopontine angle. Currently, the manual extraction of linear features by neuroradiologists is time-consuming and subjective. The authors propose using artificial intelligence (AI) tools to automatically detect and segment VS, as well as extract clinically relevant features. The results of their study show a significant correlation between the manual and automated measurements. This framework has the potential to improve patient outcomes and support clinical decision-making in the management of VS. [Extracted from the article] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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