Machine learning to predict in-stent stenosis after Pipeline embolization device placement
BackgroundThe Pipeline embolization device (PED) is a flow diverter used to treat intracranial aneurysms. In-stent stenosis (ISS) is a common complication of PED placement that can affect long-term outcome. This study aimed to establish a feasible, effective, and reliable model to predict ISS using...
Main Authors: | Dachao Wei, Dingwei Deng, Siming Gui, Wei You, Junqiang Feng, Xiangyu Meng, Xiheng Chen, Jian Lv, Yudi Tang, Ting Chen, Peng Liu |
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Format: | Article |
Language: | English |
Published: |
Frontiers Media S.A.
2022-09-01
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Series: | Frontiers in Neurology |
Subjects: | |
Online Access: | https://www.frontiersin.org/articles/10.3389/fneur.2022.912984/full |
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