AI and Glaucoma

Recent artificial intelligence advances in ophthalmology, especially deep learning (DL), have shown enormous potential with vast quantities of data. Unlike diabetic retinopathy and age-related macular degeneration where early DL initiatives in ophthalmology focused on, there have been limited but expanding efforts for utilizing DL algorithms to improve diagnosis and management of glaucoma. In this chapter, we provide a summary of current AI applications, challenges, and state-of-the-art DL systems in glaucoma. Key applications involve glaucoma diagnosis, longitudinal progression analysis, structural-functional correlation investigation, scan enhancement, and new knowledge discovery. Given the multifactorial etiology of glaucoma, various input modalities such as fundus photographs, optical coherence tomography (OCT), visual field testing, and demographic data have been investigated along with DL models. The ability of extracting meaningful representations from high dimensional and complex multi-modal data enables DL system to achieve high accuracy of glaucoma diagnosis and prognosis and to discover new knowledges to improve our current understanding of glaucoma. Though DL algorithms have shown promising improvements in performing tasks related to glaucoma, lack of large dataset, diversity of data formations and evaluation criteria, and poor model interpretability still remain as challenges to the research community.