Artificial Intelligence for the Detection of Diabetic Retinopathy

Abstract

Screening and timely treatment can avoid the majority of severe vision loss and blindness from diabetic retinopathy. Artificial intelligence (AI) algorithms that detect DR from retinal photographs without human assessment might reduce the challenges of systematic screening. The German National Care Guideline recommends that individuals with diabetes receive annual or biennial eye examinations to detect treatable DR. Efficient and comprehensive screening of the growing diabetic population requires more and more resources. Artificial intelligence (AI) algorithms that detect DR from retinal photographs without human assessment might help in coping with the immense screening burden. Many of these AI algorithms have achieved good sensitivity and specificity for detecting treatable DR, as compared to human graders; however, many important challenges remain, such as acceptance, cost-effectiveness, liability issues, IT security, and reimbursement. AI-supported DR screening has so far only been used to a limited extent, even in countries with a developed digital infrastructure. These questions must be addressed before AI-based DR screening can be implemented on a large scale into clinical practice. This overview presents key concepts in development and currently approved AI applications for DR screening.