An extensive analysis of machine learning techniques for identifying glaucoma.

Area of Science:

• Ophthalmology
• Medical Imaging
• Artificial Intelligence

Background:

• Glaucoma is a leading cause of irreversible blindness worldwide.
• Early detection of glaucoma is critical to prevent vision loss.
• Machine learning (ML) algorithms are increasingly used for glaucoma diagnosis.

Purpose of the Study:

• To review recent advancements in ML algorithms for glaucoma diagnosis.
• To analyze the performance, limitations, and clinical applications of these ML models.
• To identify future research directions for improving ML in glaucoma detection.

Main Methods:

• Systematic review of 30 papers published between 2019 and 2024.
• Analysis of ML algorithms, imaging modalities (fundus images, OCT), and data integration strategies.
• Evaluation of model performance, generalizability, and explainability.

Main Results:

• Combining imaging data (fundus, OCT) with ML models shows promising diagnostic results.
• Persistent challenges include poor multi-modal data integration, limited generalizability, and lack of explainability.
• Research activity in ML for glaucoma diagnosis has increased significantly from 2019-2023.

Conclusions:

• Multi-modal techniques, interpretable models, and robust datasets are essential for accurate glaucoma diagnosis.
• Standardized frameworks and diverse datasets are recommended for clinical acceptance of ML tools.
• ML holds significant current and future potential for glaucoma detection and management.