Machine learning to predict in-stent stenosis after Pipeline embolization device placement

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...

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Bibliographic Details
Main Authors: Dachao Wei, Dingwei Deng, Siming Gui, Wei You, Junqiang Feng, Xiangyu Meng, Xiheng Chen, Jian Lv, Yudi Tang, Ting Chen, Peng Liu
Format: Article
Language:English
Published: Frontiers Media S.A. 2022-09-01
Series:Frontiers in Neurology
Subjects:
machine learning
flow diverter
Pipeline embolization device
complication
endovascular treatment
intracranial aneurysm
Online Access:https://www.frontiersin.org/articles/10.3389/fneur.2022.912984/full

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https://www.frontiersin.org/articles/10.3389/fneur.2022.912984/full

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