Challenges for ocular disease identification in the era of artificial intelligence

Retinal image analysis is an integral and fundamental step towards the identification and classification of ocular diseases like glaucoma, diabetic retinopathy, macular edema, and cardiovascular diseases through computer-aided diagnosis systems. Various abnormalities are observed through retinal image modalities like fundus, fluorescein angiography, and optical coherence tomography by ophthalmologists, and computer science professionals. Retinal image analysis has gained a lot of importance in recent years due to advances in computational, storage, and image acquisition technologies. Better computational capabilities lead to a rise in the implementation of deep learning-based methods for ocular disease detection. Although deep learning promises better performance in this field, some issues like lack of well-labeled datasets, unavailability of large enough datasets, class imbalance, and model generalizability are yet to be addressed. Also, the real-time implementation of detection methods on new devices or existing hardware is an untouched area. This article highlights the development of retinal image analysis and related issues due to the introduction of AI-based methods. The methods are analyzed in terms of standard performance metrics on various publicly and privately available datasets.