An AI-based auxiliary empirical antibiotic therapy model for children with bacterial pneumonia using low-dose chest CT images

PURPOSE: To construct an auxiliary empirical antibiotic therapy (EAT) multi-class classification model for children with bacterial pneumonia using radiomics features based on artificial intelligence and low-dose chest CT images. MATERIALS AND METHODS: Data were retrospectively collected from childre...

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Detalles Bibliográficos
Autores principales: Zhang, Mudan, Yu, Siwei, Yin, Xuntao, Zeng, Xianchun, Liu, Xinfeng, Shen, ZhiYan, Zhang, Xiaoyong, Huang, Chencui, Wang, Rongpin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Singapore 2021
Materias:
Original Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8490241/
https://www.ncbi.nlm.nih.gov/pubmed/34101118
http://dx.doi.org/10.1007/s11604-021-01136-2

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8490241/
https://www.ncbi.nlm.nih.gov/pubmed/34101118
http://dx.doi.org/10.1007/s11604-021-01136-2

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