# Automatic detection of glaucoma via fundus imaging and artificial intelligence: A review

arXiv: https://arxiv.org/abs/2204.05591 · HTML: https://mlsanity.com/paper/2204.05591/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 125 references that are in the map.
- First posted on arXiv 12 Apr 2022.

## Builds on (most cited first, 6 of 6)

- [A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges](https://mlsanity.com/paper/2011.06225/) (2020)
- [This Looks Like That: Deep Learning for Interpretable Image Recognition](https://mlsanity.com/paper/1806.10574/) (2018)
- [Transformer Interpretability Beyond Attention Visualization](https://mlsanity.com/paper/2012.09838/) (2020)
- [Understanding artificial intelligence ethics and safety](https://mlsanity.com/paper/1906.05684/) (2019)
- [Model-Based and Data-Driven Strategies in Medical Image Computing](https://mlsanity.com/paper/1909.10391/) (2019)
- [Deep learning on fundus images detects glaucoma beyond the optic disc](https://mlsanity.com/paper/2103.11895/) (2021)

## Built on by (most cited first, 5 of 5)

- [Mask of truth: model sensitivity to unexpected regions of medical images](https://mlsanity.com/paper/2412.04030/) (2024)
- [Deep Learning and Computer Vision for Glaucoma Detection: A Review](https://mlsanity.com/paper/2307.16528/) (2023)
- [The Eye as a Window to Systemic Health: A Survey of Retinal Imaging from Classical Techniques to Oculomics](https://mlsanity.com/paper/2505.04006/) (2025)
- [Trends, Challenges, and Future Directions in Deep Learning for Glaucoma: A Systematic Review](https://mlsanity.com/paper/2411.05876/) (2024)
- [An Attention Infused Deep Learning System with Grad-CAM Visualization for Early Screening of Glaucoma](https://mlsanity.com/paper/2505.17808/) (2025)

## Abstract (arXiv)

Glaucoma is a leading cause of irreversible vision impairment globally and cases are continuously rising worldwide. Early detection is crucial, allowing timely intervention which can prevent further visual field loss. To detect glaucoma, examination of the optic nerve head via fundus imaging can be performed, at the centre of which is the assessment of the optic cup and disc boundaries. Fundus imaging is non-invasive and low-cost; however, the image examination relies on subjective, time-consuming, and costly expert assessments. A timely question to ask is can artificial intelligence mimic glaucoma assessments made by experts. Namely, can artificial intelligence automatically find the boundaries of the optic cup and disc (providing a so-called segmented fundus image) and then use the segmented image to identify glaucoma with high accuracy. We conducted a comprehensive review on artificial intelligence-enabled glaucoma detection frameworks that produce and use segmented fundus images. We found 28 papers and identified two main approaches: 1) logical rule-based frameworks, based on a set of simplistic decision rules; and 2) machine learning/statistical modelling based frameworks. We summarise the state-of-art of the two approaches and highlight the key hurdles to overcome for artificial intelligence-enabled glaucoma detection frameworks to be translated into clinical practice.