Deep learning applications in ophthalmology and computer-aided diagnostics

Recently, artificial intelligence (AI) that is based on deep learning has gained a lot of attention. Deep learning is a new technique that has a wide range of potential uses in ophthalmology. To identify diabetic retinopathy (DR), macular edema, glaucoma, retinopathy of prematurity, and age-related macular degeneration (AMD or ARMD), DL has been utilized in optical coherence tomography, images of fundus, and visual fields in ophthalmology. DL in ocular imaging can be used along with telemedicine as an effective way to find, diagnose, and check up on serious eye problems in people who need primary care and live in residential institutions. However, there are also possible drawbacks to the use of DL in ophthalmology, such as technical and clinical difficulties, the inexplicability of algorithm outputs, medicolegal concerns, and doctor and patient resistance to the "black box" AI algorithms. In the future, DL could completely alter how ophthalmology is performed. This chapter gives a description of the cutting-edge DL systems outlined for ocular applications, possible difficulties in clinical implementation, and future directions.