Artificial Intelligence in Diabetic Eye Disease Screening

Abstract

Systematic or national screening programs for diabetic retinopathy (DR) and diabetic macular edema (DME), using digital fundus photography and optical coherence tomography (OCT), are currently implemented at primary care level, aiming to provide timely referral for vision-threatening DR and DME to ophthalmologists for timely treatment and vision loss prevention. However, interpretation of retinal images requires specialized knowledge and expertise in diabetic eye disease. Furthermore, current DR screening programs are capital- and labor-intensive, which makes it difficult to rapidly scale up and expand diabetic eye screening to meet the needs of this growing global epidemic. Deep learning (DL), a new branch of machine learning technology under the broad term of artificial intelligence (AI), has made remarkable breakthrough in medical imaging in particular for pattern recognition and image classification. In ophthalmology, AI and DL technology has been developed from big image datasets in assessment of retinal photographs for detection and screening of DR as well as the segmentation and assessment of OCT images for diagnosis and screening of DME. This review aimed to summarize the current progress and the development of using AI and DL technology for diabetic eye disease screening as well as current challenges in the actual implementation of DL in screening programs, and translating DL research into direct clinical applications of screening in a community setting.