A deep-learning system predicts glaucoma incidence and progression using retinal photographs

BACKGROUND. Deep learning has been widely used for glaucoma diagnosis. However, there is no clinically validated algorithm for glaucoma incidence and progression prediction. This study aims to develop a clinically feasible deep-learning system for predicting and stratifying the risk of glaucoma onse...

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Veröffentlicht in:	The Journal of clinical investigation 2022-06, Vol.132 (11), p.1-10
Hauptverfasser:	Li, Fei, Su, Yuandong, Lin, Fengbin, Li, Zhihuan, Song, Yunhe, Nie, Sheng, Xu, Jie, Chen, Linjiang, Chen, Shiyan, Li, Hao, Xue, Kanmin, Che, Huixin, Chen, Zhengui, Yang, Bin, Zhang, Huiying, Ge, Ming, Zhong, Weihui, Yang, Chunman, Chen, Lina, Wang, Fanyin, Jia, Yunqin, Li, Wanlin, Wu, Yuqing, Li, Yingjie, Gao, Yuanxu, Zhou, Yong, Zhang, Kang, Zhang, Xiulan
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Artificial intelligence Biomedical research Color vision Datasets Deep learning Disease Feasibility studies Field study Glaucoma Medical personnel Medical screening Smartphones
Online-Zugang:	Volltext
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