Background: Glaucoma is the leading cause of irreversible blindness worldwide. Several techniques exist for the diagnosis and follow-up of patients. Optical coherence tomography (OCT) angiography (OCTA) is a recently developed technique that provides a quantitative assessment of the microcirculation of the retina and choroid in a fast, noninvasive way. Despite it being a novel technique, several publications have already been done in the glaucoma field. However, a summary of findings is currently lacking. Aims: To perform a literature review to assess the role of OCTA in glaucoma diagnosis and follow-up. Methods: A database search was carried out using MEDLINE, Embase, and Web of Science, including all original works registered until July 23, 2017. Results: OCTA (1) has a high repeatability and reproducibility, (2) has good discriminatory power to differentiate normal eyes from glaucoma eyes, (3) is more strongly correlated with visual function than conventional OCT, (4) has good discriminatory power to differentiate early-glaucoma eyes from normal eyes (i.e., at least equal to that of OCT), (5) reaches a floor effect at a more advanced disease stage than OCT, and (6) is able to detect progression in glaucoma eyes. Conclusion: OCTA shows potential to become a part of everyday glaucoma management.

A plethora of classification models for the detection of glaucoma from fundus images have been proposed in recent years. Often trained with data from a single glaucoma clinic, they report impressive performance on internal test sets, but tend to struggle in generalizing to external sets. This performance drop can be attributed to data shifts in glaucoma prevalence, fundus camera, and the definition of glaucoma ground truth. In this study, we confirm that a previously described regression network for glaucoma referral (G-RISK) obtains excellent results in a variety of challenging settings. Thirteen different data sources of labeled fundus images were utilized. The data sources include two large population cohorts (Australian Blue Mountains Eye Study, BMES and German Gutenberg Health Study, GHS) and 11 publicly available datasets (AIROGS, ORIGA, REFUGE1, LAG, ODIR, REFUGE2, GAMMA, RIM-ONEr3, RIM-ONE DL, ACRIMA, PAPILA). To minimize data shifts in input data, a standardized image processing strategy was developed to obtain 30° disc-centered images from the original data. A total of 149,455 images were included for model testing. Area under the receiver operating characteristic curve (AUC) for BMES and GHS population cohorts were at 0.976 [95% CI: 0.967–0.986] and 0.984 [95% CI: 0.980–0.991] on participant level, respectively. At a fixed specificity of 95%, sensitivities were at 87.3% and 90.3%, respectively, surpassing the minimum criteria of 85% sensitivity recommended by Prevent Blindness America. AUC values on the eleven publicly available data sets ranged from 0.854 to 0.988. These results confirm the excellent generalizability of a glaucoma risk regression model trained with homogeneous data from a single tertiary referral center. Further validation using prospective cohort studies is warranted.

Glaucoma is the leading cause of irreversible but preventable blindness in the world. Its major treatable risk factor is the intra-ocular pressure, although other biomarkers are being explored to improve the understanding of the pathophysiology of the disease. It has been recently observed that glaucoma induces changes in the ocular hemodynamics. However, its effects on the functional behavior of the retinal arterioles have not been studied yet. In this paper we propose a first approach for characterizing those changes using computational hemodynamics. The retinal blood flow is simulated using a 0D model for a steady, incompressible non Newtonian fluid in rigid domains. The simulation is performed on patient-specific arterial trees extracted from fundus images. We also propose a novel feature representation technique to comprise the outcomes of the simulation stage into a fixed length feature vector that can be used for classification studies. Our experiments on a new database of fundus images show that our approach is able to capture representative changes in the hemodynamics of glaucomatous patients. Code and data are publicly available in https://ignaciorlando.github.io .