Feasibility of automated artificial intelligence screening for diabetic retinopathy in a resource-limited setting

Background: Diabetic retinopathy (DR), a leading cause of blindness, requires effective screening, which is challenging in resource-limited settings. While artificial intelligence (AI) screening offers potential, real-world feasibility data are scarce. Methods: In this prospective feasibility study, diabetic adults at a Cape Town public hospital endocrine clinic underwent DR screening with an autonomous AI system (LumineticsCore®) between April and July 2022. Ungradable images or AI-detected referable DR (moderate non-proliferative DR or worse) prompted ophthalmologist referral. Screening time, ungradable rates and referral burden were assessed. Results: Sixty-two patients were screened, with a mean AI screening time of 11.7 min. Initial non-mydriatic images were ungradable in 39/62 (62.9%), and 19/62 (30.6%) remained ungradable despite dilatation. Overall, 55/62 (88.7%) were referred to ophthalmology, including 36 (58.1%) for AI-referable DR and 19 (30.6%) for ungradable images. Ophthalmologist assessment found that 8/62 (12.9%) required DR treatment, corresponding to a number needed to screen (NNS) of 7.8 (95% CI, 4.2 -17.9). Cataract was the main cause of AI-ungradable images. Conclusion: AI screening time was acceptable and identified vision-threatening DR requiring treatment at a meaningful rate (about one in eight screened). However, a high initial referral burden and many ungradable images (mainly because of cataract) could overwhelm ophthalmology services without pathway adaptation. Contribution: This study provides feasibility data on autonomous AI screening for DR in a South African public-sector clinic. Findings highlight the need for context-specific adaptations, such as raising the referral threshold to vision-threatening DR (severe non-proliferative DR, proliferative DR and/or diabetic macular oedema) and integrating protocols for managing cataract-related ungradable images, to support sustainable implementation.