Development and validation of a real-time artificial intelligence-assisted system for detecting early gastric cancer: A multicentre retrospective diagnostic study

BACKGROUND: We aimed to develop and validate a real-time deep convolutional neural networks (DCNNs) system for detecting early gastric cancer (EGC). METHODS: All 45,240 endoscopic images from 1364 patients were divided into a training dataset (35823 images from 1085 patients) and a validation datase...

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Detalles Bibliográficos
Autores principales: Tang, Dehua, Wang, Lei, Ling, Tingsheng, Lv, Ying, Ni, Muhan, Zhan, Qiang, Fu, Yiwei, Zhuang, Duanming, Guo, Huimin, Dou, Xiaotan, Zhang, Wei, Xu, Guifang, Zou, Xiaoping
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2020
Materias:
Research Paper
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7708824/
https://www.ncbi.nlm.nih.gov/pubmed/33254026
http://dx.doi.org/10.1016/j.ebiom.2020.103146

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7708824/
https://www.ncbi.nlm.nih.gov/pubmed/33254026
http://dx.doi.org/10.1016/j.ebiom.2020.103146

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