Artificial Intelligence and Glaucoma: Going Back to Basics

Abstract

Saif Aldeen AlRyalat,1 Praveer Singh,2 Jayashree Kalpathy-Cramer,2 Malik Y Kahook2 1Department of Ophthalmology, The University of Jordan, Amman, 11942, Jordan; 2Department of Ophthalmology, University of Colorado School of Medicine, Sue Anschutz-Rodgers Eye Center, Aurora, CO, USACorrespondence: Malik Y Kahook, Department of Ophthalmology, University of Colorado School of Medicine, Sue Anschutz-Rodgers Eye Center, Aurora, CO, USA, Email [email protected]: 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

Keywords

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