Glaucoma is one of the most severe eye diseases, characterized by rapid progression and leading to irreversible blindness. It is often the case that diagnostics is carried out when one's sight has already significantly degraded due to the lack of noticeable symptoms at early stage of the disease. Regular glaucoma screenings of the population shall improve early-stage detection, however the desirable frequency of etymological checkups is often not feasible due to the excessive load imposed by manual diagnostics on limited number of specialists. Considering the basic methodology to detect glaucoma is to analyze fundus images for the optic-disc-to-optic-cup ratio, Machine Learning algorithms can offer sophisticated methods for image processing and classification. In our work, we propose an advanced image pre-processing technique combined with a multi-view network of deep classification models to categorize glaucoma. Our Glaucoma Automated Retinal Detection Network (GARDNet) has been successfully tested on Rotterdam Eye-PACS AIROGS dataset with an AUC of 0.92, and then additionally fine-tuned and tested on RIM-ONE DL dataset with an AUC of 0.9308 outperforming the state-of-the-art of 0.9272.

Glaucoma, one of the world's leading causes of irreversible blindness, is expected to affect an estimated 120 million by 2040, globally. Treatment options for management of Referable Glaucoma (a stage of the disease where immediate treatment can help manage the disease better) exist, but a simple test to screen for this has been elusive. Today, the only way to detect those with Referable Glaucoma is a series of complex investigations often requiring multiple devices handled by Glaucoma Specialists. Remidio Innovative Solutions, together with Aravind Eye Hospital (AEH), Pondicherry, and Narayana Nethralaya (NN), Bangalore, announced the results of a landmark clinical trial that can help revolutionize the detection of Referable Glaucoma. The integrated solution provides an instant report in contexts where there are no specialists.

The development of automatic tools for early glaucoma diagnosis with color fundus photographs can significantly reduce the impact of this disease. However, current state-of-the-art solutions are not robust to real-world scenarios, providing over-confident predictions for out-of-distribution cases. With this in mind, we propose a model based on the Dirichlet distribution that allows to obtain class-wise probabilities together with an uncertainty estimation without exposure to out-of-distribution cases. We demonstrate our approach on the AIROGS challenge. At the start of the final test phase (8 Feb. 2022), our method had the highest average score among all submissions.