An extensive analysis of machine learning techniques for identifying glaucoma.

Glaucoma is a progressive optic neuropathy, and it's one of the leading causes of permanent blindness in the world. Machine learning (ML) algorithms have recently emerged as effective tools in diagnosing glaucoma, and early detection is crucial in the prevention of vision loss. In this review, the recent developments in ML algorithms in the diagnosis of glaucoma, their performance, drawbacks, and clinical applications are analyzed. There have been promising results when combining imaging modes including fundus images and optical coherence tomography (OCT) with machine learning models. However, problems persist in poor multi-modal data integration, limited generalizability, and a lack of explainability. The importance of multi-modal techniques, interpretable models, and robust datasets to enhance the diagnostic accuracy and reliability of ML algorithms is stressed by this study. There are many recommendations for further study to achieve clinical acceptability, which include building standardized frameworks and controlling diversity in data. Therefore, it highlights the current and future potential of ML regarding glaucoma. A total of 30 papers were reviewed from 2019 to 2024, with an increase in research activity. From 2019 to 2021, there were 3 papers reviewed per year, and it increased to 5 in 2022, peaked at 9 in 2023, and then decreased to 7 in 2024, reflecting growing yet fluctuating interest in this topic. The pattern depicts growing interest in the field and body of literature.