AI-Based Prediction and Detection of Glaucoma Using Fundus Imaging: A Review of Machine and Deep Learning Approaches

Abstract

Glaucoma is an acquired chronic neuropathy characterized by damage to the optic nerve head and retinal nerve fiber layer. It is a leading cause of irreversible blindness worldwide. Our paper presents a systematic review of recent machine learning (ML) and deep learning (DL) approaches for glaucoma diagnosis from retinal fundus images. We survey available datasets, preprocessing methods, network architectures, and evaluation metrics. The review highlights automated methods for optic nerve segmentation and glaucoma classification, many achieving high accuracy. Results are synthesized to discuss the strengths and limitations of current AI methods and suggest directions for future research.