Development and clinical deployment of a smartphone-based visual field deep learning system for glaucoma detection

Abstract By 2040, ~100 million people will have glaucoma. To date, there are a lack of high-efficiency glaucoma diagnostic tools based on visual fields (VFs). Herein, we develop and evaluate the performance of ‘iGlaucoma’, a smartphone application-based deep learning system (DLS) in detecting glauco...

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Auteurs principaux:	Fei Li, Diping Song, Han Chen, Jian Xiong, Xingyi Li, Hua Zhong, Guangxian Tang, Sujie Fan, Dennis S. C. Lam, Weihua Pan, Yajuan Zheng, Ying Li, Guoxiang Qu, Junjun He, Zhe Wang, Ling Jin, Rouxi Zhou, Yunhe Song, Yi Sun, Weijing Cheng, Chunman Yang, Yazhi Fan, Yingjie Li, Hengli Zhang, Ye Yuan, Yang Xu, Yunfan Xiong, Lingfei Jin, Aiguo Lv, Lingzhi Niu, Yuhong Liu, Shaoli Li, Jiani Zhang, Linda M. Zangwill, Alejandro F. Frangi, Tin Aung, Ching-yu Cheng, Yu Qiao, Xiulan Zhang, Daniel S. W. Ting
Format:	article
Langue:	EN
Publié:	Nature Portfolio 2020
Sujets:	Computer applications to medicine. Medical informatics R858-859.7
Accès en ligne:	https://doaj.org/article/481e146d7ee14aa8bb8e0910e851c4af
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Development and clinical deployment of a smartphone-based visual field deep learning system for glaucoma detection

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