Glaucoma Detection Using Machine Learning: A Review

DOI

10.2991/978-94-6239-727-9_21How to use a DOI?

Keywords

Glaucoma detection; Machine learning (ML); Deep learning (DL); Fundus imaging; Optical coherence tomography (OCT)

Abstract

One of the main causes of permanent blindness in the globe is glaucoma, and successful treatment depends on early detection. Automated glaucoma identification utilising fundus and optical coherence tomography (OCT) images has been made possible by recent developments in machine learning (ML) and deep learning (DL), which have demonstrated promising diagnostic potential. This review critically examines techniques, datasets, performance outcomes, and limitations in order to synthesise findings from current studies. Although a lot of models claim to be highly accurate at binary classification, their dependence on tiny, unbalanced, or single-center datasets limits their ability to be applied to a variety of populations. Furthermore, the majority of methods ignore progression prediction and severity rating, which are critical for clinical decision-making. In ophthalmology, DL’s black-box status further restricts acceptance and confidence. In addition to outlining potential possibilities towards reliable, explicable, and clinically validated AI frameworks for real-world glaucoma detection and care, this review identifies present research gaps.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Divya Panchal
AU  - Swapnil Parikh
AU  - Sanjay Agal
PY  - 2026
DA  - 2026/07/22
TI  - Glaucoma Detection Using Machine Learning: A Review
BT  - Proceedings of the International Conference on Sustainable Micro-Nano Materials & Innovative Technology (ICSUMMIT 2026)
PB  - Atlantis Press
SP  - 279
EP  - 286
SN  - 2589-4943
UR  - https://doi.org/10.2991/978-94-6239-727-9_21
DO  - 10.2991/978-94-6239-727-9_21
ID  - Panchal2026
ER  -