Artificial Intelligence and Corneal Diseases

Linda Kang; Dena Ballouz; Maria A. Woodward


Curr Opin Ophthalmol. 2022;33(5):407-417. 

In This Article

Abstract and Introduction


Purpose of Review: Artificial intelligence has advanced rapidly in recent years and has provided powerful tools to aid with the diagnosis, management, and treatment of ophthalmic diseases. This article aims to review the most current clinical artificial intelligence applications in anterior segment diseases, with an emphasis on microbial keratitis, keratoconus, dry eye syndrome, and Fuchs endothelial dystrophy.

Recent Findings: Most current artificial intelligence approaches have focused on developing deep learning algorithms based on various imaging modalities. Algorithms have been developed to detect and differentiate microbial keratitis classes and quantify microbial keratitis features. Artificial intelligence may aid with early detection and staging of keratoconus. Many advances have been made to detect, segment, and quantify features of dry eye syndrome and Fuchs. There is significant variability in the reporting of methodology, patient population, and outcome metrics.

Summary: Artificial intelligence shows great promise in detecting, diagnosing, grading, and measuring diseases. There is a need for standardization of reporting to improve the transparency, validity, and comparability of algorithms.


Advances in artificial intelligence have generated novel insights and are transforming screening, diagnosis, and treatment in various medical fields. Artificial intelligence in ophthalmology has expanded significantly in the last decade. The eye community is well positioned to create artificial intelligence strategies given the broad use of imaging tools in clinical practice and hence the availability of codified data from imaging to numeric clinical parameters (e.g., visual acuity, intraocular pressure, etc.). Image-based ophthalmic artificial intelligence began by focusing on eye diseases involving the posterior segment, such as macular degeneration, diabetic retinopathy, and glaucoma, due to the population prevalence and use of ophthalmic imaging in routine clinical practice.[1–4] This led to advances in medicine – the first autonomous artificial intelligence-based diagnostic tool approved by the Food and Drug Administration was for detecting diabetic retinopathy.[5]

These advances have inspired artificial intelligence development to address diagnostic and management concerns for diseases of the anterior segment. Artificial intelligence algorithms for anterior segment conditions have been reviewed in the past.[6–14] This review article focuses on advancements in the past 18 months for the use of artificial intelligence for corneal diseases.