A Systematic Review of Deep Learning Methods Applied to Ocular Images

Artificial intelligence is having an important effect on different areas of medicine, and ophthalmology is not the exception. In particular, deep learning methods have been applied successfully to the detection of clinical signs and the classification of ocular diseases. This represents a great potential to increase the number of people correctly diagnosed. In ophthalmology, deep learning methods have primarily been applied to eye fundus images and optical coherence tomography. On the one hand, these methods have achieved outstanding performance in the detection of ocular diseases such as diabetic retinopathy, glaucoma, diabetic macular degeneration, and age-related macular degeneration. On the other hand, several worldwide challenges have shared big eye imaging datasets with the segmentation of part of the eyes, clinical signs, and ocular diagnoses performed by experts. In addition, these methods are breaking the stigma of black-box models, with the delivery of interpretable clinical information. This review provides an overview of the state-of-the-art deep learning methods used in ophthalmic images, databases, and potential challenges for ocular diagnosis.