Review of Smartphone Deep Learning Applications Using Eye Imaging Diagnostic Techniques in Ophthalmology

Eye diseases, which often lead to severe vision loss, can be prevented, reversed, or slowed down, if diagnosed early. The demand for accurate and timely diagnosis has stimulated the exploration of innovative diagnostic techniques, including the connection of deep learning with smartphone applications to analyze eye images. This review explores how smartphone-based systems transform diagnostic processes using built-in cameras, computational resources, and deep learning models to provide inexpensive solutions for early detection on a massive scale.We provide a comprehensive analysis of recent advances in smartphone deep learning applications using eye imaging diagnostic techniques in ophthalmology, highlighting the key application areas and examining the underlying methodologies, hardware, and software solutions used, as well as their clinical implications.We describe 26 complete diagnostic solutions consisting of the smartphone application with the ability to capture images using the smartphone and the accessible deep learning model, supplemented with 48 partial solutions. The aim of this review is to present an overview of current solutions and provide a comprehensive view of future directions for technicians and ophthalmologists in this emerging interdisciplinary research field.