AI in Glaucoma

Practical applications for screening, diagnosis, and monitoring.

Douglas R. Costa, MD, PhD; Felipe A. Medeiros, MD, PhD; and Rafael Scherer, MD, PhD

Glaucoma Today

Key Takeaways Recent research on the use of AI in glaucoma care is focused on clinically meaningful applications, such as screening, diagnosis, and detecting disease progression. As these systems move from research settings to clinical practice, it is essential to identify how AI can improve care and as well as its current limitations. Key challenges of adopting AI for routine clinical use include ensuring its generalizability across patient populations, integrating algorithms into clinical workflows, and demonstrating that it can improve patient outcomes. The use of AI, particularly deep learning models, has expanded rapidly in ophthalmology during the past decade. Publications on combining glaucoma and AI increased considerably during this period, reflecting a growing interest in applying machine learning methods to imaging, perimetry, and other clinical data. Early work concentrated largely on proof-of-concept algorithms designed to classify eyes as either glaucomatous or healthy. Recently, the focus of research has shifted toward more clinically meaningful applications, such as objective screening, increasing diagnostic accuracy in practice, and the detection of disease progression. As these systems move from research settings into clinical practice, it is essential to identify both how AI could realistically improve care and its current limitations. GLAUCOMA SCREENING Effective population-based screening programs are limited by the lack of a cost-effective screening test that offers high diagnostic accuracy. Traditional approaches based on IOP measurement or visual field tests have limited sensitivity and specificity in population settings, and many individuals with glaucoma remain undiagnosed until their disease reaches an advanced stage.

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