Artificial Intelligence and Glaucoma: Going Back to Basics

Abstract

There has been a recent surge in the number of publications centered on the use of artificial intelligence (AI) to diagnose various systemic diseases. The Food and Drug Administration has approved several algorithms for use in clinical practice. In ophthalmology, most advances in AI relate to diabetic retinopathy, which is a disease process with agreed upon diagnostic and classification criteria. However, this is not the case for glaucoma, which is a relatively complex disease without agreed-upon diagnostic criteria. Moreover, currently available public datasets that focus on glaucoma have inconstant label quality, further complicating attempts at training AI algorithms efficiently. In this perspective paper, we discuss specific details related to developing AI models for glaucoma and suggest potential steps to overcome current limitations.

Keywords:

• artificial intelligence
• glaucoma
• deep learning
• optic disc
• segmentation

Disclosure

Professor Jayashree Kalpathy-Cramer reports grants from NIH, Genentech, and GE healthcare; consultant for Silioam Vision LLC, outside the submitted work. The authors report no other conflicts of interest in this work.