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Glaucoma is the second leading cause of blindness worldwide. This paper proposes an automated glaucoma screening method using retinal fundus images via the ensemble technique to fuse the results of different classification networks and the result of each classification network was fed as an input to a simple artificial neural network (ANN) to obtain the final result. Three public datasets, i.e., ORIGA-650, RIM-ONE R3, and DRISHTI-GS were used for training and evaluating the performance of the proposed network. The experimental results showed that the proposed network outperformed other state-of-art glaucoma screening algorithms with AUC of 0.94. Our proposed algorithms showed promising potential as a medical support system for glaucoma screening especially in low resource countries.

SIGNIFICANCE Head-mounted video display systems and image processing as a means of enhancing low vision are ideas that have been around for more than 20 years. Recent developments in virtual and augmented reality technology and software have opened up new research opportunities that will lead to benefits for low vision patients. Since the Visionics low vision enhancement system (LVES), the first head-mounted video display LVES, was engineered 20 years ago, various other devices have come and gone with a recent resurgence of the technology over the past few years. In this article, we discuss the history of the development of LVESs, describe the current state of available technology by outlining existing systems, and explore future innovation and research in this area. Although LVESs have now been around for more than two decades, there is still much that remains to be explored. With the growing popularity and availability of virtual reality and augmented reality technologies, we can now integrate these methods within low vision rehabilitation to conduct more research on customized contrast-enhancement strategies, image motion compensation, image-remapping strategies, and binocular disparity, all while incorporating eye-tracking capabilities. Future research should use this available technology and knowledge to learn more about the visual system in the low vision patient and extract this new information to create prescribable vision enhancement solutions for the visually impaired individual.

Background. Assessment of color disc photograph (C-DP) is affected by image quality, which decreases the ability to detect glaucoma. High-dynamic-range (HDR) imaging provides a greater range of luminosity. Therefore, the objective of this study was to evaluate the capability of ophthalmology residents to detect glaucoma using HDR-concept disc photography (HDR-DP) compared to C-DP. Design. Cross-sectional study. Methods. Twenty subjects were classified by 3 glaucoma specialists as either glaucoma, glaucoma suspect, or control. All C-DPs were converted to HDR-DPs and randomly presented and assessed by 10 first-year ophthalmology residents. Sensitivity and specificity of glaucoma detection were compared. Results. The mean ± SD of averaged retinal nerve fiber layer (RNFL) thickness was 74.0 ± 6.1 μm, 100.2 ± 9.6 μm, and 105.8 ± 17.2 μm for glaucoma, glaucoma suspect, and controls, respectively. The diagnostic sensitivity of HDR-DP was higher than that of C-DP (87% versus 68%, mean difference: 19.0, 95% CI: 4.91 to 33.1; p=0.014). Regarding diagnostic specificity, HDR-DP and C-DP yielded 46% and 75% (mean difference: 29.0, 95% CI: 13.4 to 44.6; p=0.002). Conclusions. HDR-DP statistically increased diagnostic sensitivity but not specificity. HDR-DP may be a screening tool for nonexpert ophthalmologists.