Machine Learning Application With Quantitative Digital Subtraction Angiography for Detection of Hemorrhagic Brain Arteriovenous Malformations

Machine Learning Application With Quantitative Digital Subtraction Angiography for Detection of Hemorrhagic Brain Arteriovenous Malformations

Clinical features are the primary measures used for risk assessment of cerebrovascular diseases. However, clinical features, especially angioarchitecture, in digital subtraction angiography require further interpretation by specialized radiologists. This approach for risk assessment requires multiva...

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Bibliographic Details
Main Authors: Jia-Sheng Hong, Chung-Jung Lin, Yue-Hsin Lin, Cheng-Chia Lee, Huai-Che Yang, Ling-Hsuan Meng, Te-Ming Lin, Yong-Sin Hu, Wan-Yuo Guo, Wei-Fa Chu, Yu-Te Wu
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Brain arteriovenous malformation
digital subtraction angiography
machine learning
quantitative analysis
rupture risk
time-density curve
Online Access:https://ieeexplore.ieee.org/document/9252182/

Internet

https://ieeexplore.ieee.org/document/9252182/

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