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PurposeTo assess the 10-year incidence of open-angle glaucoma (OAG) and its associations in an adult Chinese population.MethodsLongitudinal observational population-based study. Out of 4439 participants aged 40+ years participating in the Beijing Eye Study in 2001, 2695 individuals (60.7%) were re-examined in 2011, while 397 participants had died (8.5%).ResultsIncident OAG was found in 75 participants among 2494 individuals free of glaucoma at baseline. The 10-year OAG incidence (mean: 3.0%; 95% CI 2.5 to 3.5) increased from 1.8% (95% CI 1.3 to 2.4) in individuals aged 40–49 years, to 5.9% (95% CI 3.1 to 9.6) in participants aged 70+ years. OAG incidence was highest in the high myopia group (13.3%±6.3%, OR: 7.3; 95% CI 3.3 to 16.3), followed by the moderately myopic group (8.1%±4.3%, OR: 4.2; 95% CI 2.0 to 8.8) and the low myopic group (6.2%±2.8%, OR: 3.2; 95% CI 1.7 to 5.8), as compared with the emmetropic/hyperopic group (2.1%±0.8%). In multivariable analysis, higher OAG incidence was associated with older age (OR: 1.06; 95% CI 1.03 to 1.09), longer axial length (OR: 1.72; 95% CI 1.45 to 2.05), higher intraocular pressure (IOP) in 2001 (OR: 1.18; 95% CI 1.08 to 1.29), higher vertical cup/disc ratio (VCDR) (OR: 60.8; 95% CI 6.7 to 556) and thinner central corneal thickness (CCT) (OR: 0.98; 95% CI 0.97 to 0.99).ConclusionsIn a 10-year follow-up, high myopia was a major risk factor for the development of OAG with a 7.3-fold risk increase as compared with emmetropic eyes. Higher age, IOP, VCDR and thinner CCT were additionally related with an increased OAG incidence. The findings may be of importance to clinical protocols and screening strategies.

From diagnosing cardiovascular diseases to analyzing the progression of diabetic retinopathy, accurate retinal artery/vein (A/V) classification is critical. Promising approaches for A/V classification, ranging from conventional graph based methods to recent convolutional neural network (CNN) based models, have been known. However, the inability of traditional graph based methods to utilize deep hierarchical features extracted by CNNs and the limitations of current CNN based methods to incorporate vessel topology information hinder their effectiveness. In this paper, we propose a new CNN based framework, VTG-Net (vessel topology graph network), for retinal A/V classification by incorporating vessel topology information. VTG-Net exploits retinal vessel topology along with CNN features to improve A/V classification accuracy. Specifically, we transform vessel features extracted by CNN in the image domain into a graph representation preserving the vessel topology. Then by exploiting a graph convolutional network (GCN), we enable our model to learn both CNN features and vessel topological features simultaneously. The final predication is attained by fusing the CNN and GCN outputs. Using a publicly available AV-DRIVE dataset and an in-house dataset, we verify the high performance of our VTG-Net for retinal A/V classification over state-of-the-art methods (with ~2% improvement in accuracy on the AV-DRIVE dataset).