# AI-Driven Approaches for Glaucoma Detection -- A Comprehensive Review

arXiv: https://arxiv.org/abs/2410.15947 · HTML: https://mlsanity.com/paper/2410.15947/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 246 references that are in the map.
- First posted on arXiv 21 Oct 2024.

## Builds on (most cited first, 18 of 18)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Deep Learning for Medical Image Analysis](https://mlsanity.com/paper/1708.08987/) (2017)
- [REFUGE Challenge: A Unified Framework for Evaluating Automated Methods for Glaucoma Assessment from Fundus Photographs](https://mlsanity.com/paper/1910.03667/) (2019)
- [Attention Based Glaucoma Detection: A Large-scale Database and CNN Model](https://mlsanity.com/paper/1903.10831/) (2019)
- [Patch-based Output Space Adversarial Learning for Joint Optic Disc and Cup Segmentation](https://mlsanity.com/paper/1902.07519/) (2019)
- [Disc-aware Ensemble Network for Glaucoma Screening from Fundus Image](https://mlsanity.com/paper/1805.07549/) (2018)
- [AIROGS: Artificial Intelligence for RObust Glaucoma Screening Challenge](https://mlsanity.com/paper/2302.01738/) (2023)
- [G1020: A Benchmark Retinal Fundus Image Dataset for Computer-Aided Glaucoma Detection](https://mlsanity.com/paper/2006.09158/) (2020)
- [AGE Challenge: Angle Closure Glaucoma Evaluation in Anterior Segment Optical Coherence Tomography](https://mlsanity.com/paper/2005.02258/) (2020)
- [FUTURE-AI: Guiding Principles and Consensus Recommendations for Trustworthy Artificial Intelligence in Medical Imaging](https://mlsanity.com/paper/2109.09658/) (2021)
- [Dataset and Evaluation algorithm design for GOALS Challenge](https://mlsanity.com/paper/2207.14447/) (2022)
- [A feature agnostic approach for glaucoma detection in OCT volumes](https://mlsanity.com/paper/1807.04855/) (2018)
- [Real-Time Glaucoma Detection from Digital Fundus Images using Self-ONNs](https://mlsanity.com/paper/2109.13604/) (2021)
- [Two-stage framework for optic disc localization and glaucoma classification in retinal fundus images using deep learning](https://mlsanity.com/paper/2005.14284/) (2020)
- [From Machine to Machine: An OCT-trained Deep Learning Algorithm for Objective Quantification of Glaucomatous Damage in Fundus Photographs](https://mlsanity.com/paper/1810.10343/) (2018)
- [Fully Convolutional Networks for Monocular Retinal Depth Estimation and Optic Disc-Cup Segmentation](https://mlsanity.com/paper/1902.01040/) (2019)
- [Dynamic region proposal networks for semantic segmentation in automated glaucoma screening](https://mlsanity.com/paper/2105.11364/) (2021)

## Abstract (arXiv)

The diagnosis of glaucoma plays a critical role in the management and treatment of this vision-threatening disease. Glaucoma is a group of eye diseases that cause blindness by damaging the optic nerve at the back of the eye. Often called "silent thief of sight", it exhibits no symptoms during the early stages. Therefore, early detection is crucial to prevent vision loss. With the rise of Artificial Intelligence (AI), particularly Deep Learning (DL) techniques, Computer-Aided Diagnosis (CADx) systems have emerged as promising tools to assist clinicians in accurately diagnosing glaucoma early. This paper aims to provide a comprehensive overview of AI techniques utilized in CADx systems for glaucoma diagnosis. Through a detailed analysis of current literature, we identify key gaps and challenges in these systems, emphasizing the need for improved safety, reliability, interpretability, and explainability. By identifying research gaps, we aim to advance the field of CADx systems especially for the early diagnosis of glaucoma, in order to prevent any potential loss of vision.