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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...
Autores principales: | Qiu, Junliang, Yan, Minbo, Wang, Haojie, Liu, Zicheng, Wang, Guojie, Wu, Xianbo, Gao, Qindong, Hu, Hongji, Chen, Junyong, Dai, Yingbo |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
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 |
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