Table 1.

Novel AI technology is used to diagnose glaucoma.

AI Method	Description	Application	Key Features
Deep Learning (DL) on Fundus Images [34]	It uses convolutional neural networks (CNNs) to analyze retinal images.	Early detection and classification of glaucoma.	High accuracy and non-invasive, can be used for mass screening.
Optical Coherence Tomography (OCT) Imaging [35,36]	AI algorithms analyze OCT scans to detect structural changes in the optic nerve head and retinal nerve fiber layer.	Diagnosis and monitoring of glaucoma progression.	High sensitivity and specificity, detailed structural analysis.
Visual Field (VF) Testing [37]	Machine learning models predict visual field loss patterns.	Functional assessment of glaucoma.	Can detect progression earlier than conventional methods.
Stereo Fundus Imaging [38]	Combines 2D images from different viewpoints to create a 3D view of the fundus.	Screening and diagnosis of glaucoma.	Provides a comprehensive view of the optic nerve head.
Bayesian Networks [39]	Uses probabilistic models to integrate various diagnostic tests and clinical data.	Comprehensive risk assessment and diagnosis.	Integrates multiple data sources and provides probabilistic outcomes.
Explainable AI (XAI) [40]	AI models that provide transparent and interpretable results.	Enhancing clinician trust and decision making.	Improves understanding of AI decisions and regulatory compliance.