A practical model for the identification of congenital cataracts using machine learning

BACKGROUND: Approximately 1 in 33 newborns is affected by congenital anomalies worldwide. We aimed to develop a practical model for identifying infants with a high risk of congenital cataracts (CCs), which is the leading cause of avoidable childhood blindness. METHODS: This case-control study was pe...

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Detalles Bibliográficos
Autores principales: Lin, Duoru, Chen, Jingjing, Lin, Zhuoling, Li, Xiaoyan, Zhang, Kai, Wu, Xiaohang, Liu, Zhenzhen, Huang, Jialing, Li, Jing, Zhu, Yi, Chen, Chuan, Zhao, Lanqin, Xiang, Yifan, Guo, Chong, Wang, Liming, Liu, Yizhi, Chen, Weirong, Lin, Haotian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2020
Materias:
Research paper
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6948173/
https://www.ncbi.nlm.nih.gov/pubmed/31901869
http://dx.doi.org/10.1016/j.ebiom.2019.102621

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6948173/
https://www.ncbi.nlm.nih.gov/pubmed/31901869
http://dx.doi.org/10.1016/j.ebiom.2019.102621

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