Cargando…
Prediction of hypertension, hyperglycemia and dyslipidemia from retinal fundus photographs via deep learning: A cross-sectional study of chronic diseases in central China
Retinal fundus photography provides a non-invasive approach for identifying early microcirculatory alterations of chronic diseases prior to the onset of overt clinical complications. Here, we developed neural network models to predict hypertension, hyperglycemia, dyslipidemia, and a range of risk fa...
Autores principales: | Zhang, Li, Yuan, Mengya, An, Zhen, Zhao, Xiangmei, Wu, Hui, Li, Haibin, Wang, Ya, Sun, Beibei, Li, Huijun, Ding, Shibin, Zeng, Xiang, Chao, Ling, Li, Pan, Wu, Weidong |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7224473/ https://www.ncbi.nlm.nih.gov/pubmed/32407418 http://dx.doi.org/10.1371/journal.pone.0233166 |
Ejemplares similares
-
Automatic Detection of Diabetic Retinopathy in Retinal Fundus Photographs Based on Deep Learning Algorithm
por: Li, Feng, et al.
Publicado: (2019) -
Automatic detection of 39 fundus diseases and conditions in retinal photographs using deep neural networks
por: Cen, Ling-Ping, et al.
Publicado: (2021) -
Predicting sex from retinal fundus photographs using automated deep learning
por: Korot, Edward, et al.
Publicado: (2021) -
A GAN-based deep enhancer for quality enhancement of retinal images photographed by a handheld fundus camera
por: Fu, Junxia, et al.
Publicado: (2022) -
Registration of OCT fundus images with color fundus photographs based on blood vessel ridges
por: Li, Ying, et al.
Publicado: (2010)