Identifying ureteral stent encrustation using machine learning based on CT radiomics features: a bicentric study

OBSTRUCTIVE: To develop and validate radiomics and machine learning models for identifying encrusted stents and compare their recognition performance with multiple metrics. METHODS: A total of 354 patients with ureteral stent placement were enrolled from two medical institutions and divided into the...

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Detalles Bibliográficos
Autores principales: Qiu, Junliang, Yan, Minbo, Wang, Haojie, Liu, Zicheng, Wang, Guojie, Wu, Xianbo, Gao, Qindong, Hu, Hongji, Chen, Junyong, Dai, Yingbo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Medicine
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10433756/
https://www.ncbi.nlm.nih.gov/pubmed/37601775
http://dx.doi.org/10.3389/fmed.2023.1202486

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10433756/
https://www.ncbi.nlm.nih.gov/pubmed/37601775
http://dx.doi.org/10.3389/fmed.2023.1202486

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