Artificial intelligence for diabetic retinopathy in low-income and middle-income countries: a scoping review

Abstract

Diabetic retinopathy (DR) is a leading cause of blindness globally. There is growing evidence to support the use of artificial intelligence (AI) in diabetic eye care, particularly for screening populations at risk of sight loss from DR in low-income and middle-income countries (LMICs) where resources are most stretched. However, implementation into clinical practice remains limited. We conducted a scoping review to identify what AI tools have been used for DR in LMICs and to report their performance and relevant characteristics. 81 articles were included. The reported sensitivities and specificities were generally high providing evidence to support use in clinical practice. However, the majority of studies focused on sensitivity and specificity only and there was limited information on cost, regulatory approvals and whether the use of AI improved health outcomes. Further research that goes beyond reporting sensitivities and specificities is needed prior to wider implementation.

Keywords: developing countries; diabetic retinopathy; information technology; public health.

Figure 1

Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 flow diagram. LMIC, low-income and middle-income country.

Figure 2

World map showing the distribution of artificial intelligence research for diabetic retinopathy in low-income and middle-income countries. The total number of studies exceeds 81; this is because some studies used data from more than one country.

Figure 3

Scatter plot showing sensitivity and specificity for the detection of referable diabetic retinopathy (DR) for artificial intelligence (AI) systems with a stated name and/or developer. Data points are color coded by AI system and are labeled with the country where either the study was undertaken or where the data used in the study are from. Only those AI systems with a stated name and/or developer and which reported sensitivity and specificity for detecting referable DR are displayed.