Artificial Intelligence in the Early Detection of Glaucoma: A Systematic Review

Daniel Atnil^*, Gusti Ayu Ning Wikan Sari, Maria Valentina Wibawa

Abstract

The application of artificial intelligence (AI), including machine learning and deep learning, demonstrates significant potential in screening, diagnosis, progression monitoring, and personalized management of glaucoma patients. Literature analysis, PICO evaluation, and QUADAS-2 bias assessment reveal that AI improves diagnostic accuracy, sensitivity, and specificity compared to conventional methods, while supporting early detection, progression prediction, and precision therapy decisions. Most studies show low risk of bias in test and flow domains, although several reviews present unclear risk in patient selection and reference standards due to heterogeneous populations and protocols. These findings highlight AI’s transformational potential in clinical glaucoma practice, yet broader validation, standardized protocols, algorithm transparency, and consideration of ethical and patient diversity aspects remain necessary.

Keywords

AI; deep learning; glaucoma; machine learning; screening

Cite This Article

Atnil, D., Sari, G. A. N. W., Wibawa, M. V. (2026). Artificial Intelligence in the Early Detection of Glaucoma: A Systematic Review. International Journal of Scientific Advances (IJSCIA), Volume 7| Issue 2: Mar – Apr 2026, Pages 217-226 URL: https://www.ijscia.com/wp-content/uploads/2026/03/Volume7-Issue2-Mar-Apr-No.1035-217-226.pdf

Volume 7 | Issue 2: Mar – Apr 2026