A multicenter study on the application of artificial intelligence radiological characteristics to predict prognosis after percutaneous nephrolithotomy

A multicenter study on the application of artificial intelligence radiological characteristics to predict prognosis after percutaneous nephrolithotomy

BackgroundA model to predict preoperative outcomes after percutaneous nephrolithotomy (PCNL) with renal staghorn stones is developed to be an essential preoperative consultation tool.ObjectiveIn this study, we constructed a predictive model for one-time stone clearance after PCNL for renal staghorn...

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Bibliographic Details
Main Authors: Jian Hou, Xiangyang Wen, Genyi Qu, Wenwen Chen, Xiang Xu, Guoqing Wu, Ruidong Ji, Genggeng Wei, Tuo Liang, Wenyan Huang, Lin Xiong
Format: Article
Language:English
Published: Frontiers Media S.A. 2023-09-01
Series:Frontiers in Endocrinology
Subjects:
artificial intelligence
clinical-radionics model
decision support system
renal staghorn stones
percutaneous nephrolithotomy
Online Access:https://www.frontiersin.org/articles/10.3389/fendo.2023.1184608/full

Internet

https://www.frontiersin.org/articles/10.3389/fendo.2023.1184608/full

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