The Role of Data Analytics and Artificial Intelligence (AI) in Ocular Telehealth

This chapter will provide a basic discussion about the theory behind machine learning techniques, including the four major models of learning—supervised, unsupervised, semisupervised, and reinforcement learning. It will then discuss the theory behind artificial neural networks and convoluted neural networks; its parallel with early visual systems, and applications of “computer vision” neural networks to tasks such as image interpretation. Deep learning applications to retinal fundus photography, ocular coherence tomography (OCT), and superhuman tasks are discussed. The chapter then shifts focus to additional research published about AI use in other ophthalmic subspecialties other than retina, and how artificial intelligence and machine learning are utilized in glaucoma, anterior segment disease (cornea/cataract), neuroophthalmology, oculoplastics, and pediatric ophthalmology. Finally, other applications of AI and big data to ocular telehealth is explored.