Artificial intelligence-supported diabetic retinopathy screening in Tanzania: a randomised controlled trial.

Abstract

Background: Non-attendance after referral from screening is one of the major challenges for diabetic retinopathy screening programmes in Africa. Artificial intelligence (AI) systems for the identification of diabetic retinopathy can provide point-of-screening results and therefore enable immediate counselling. We aimed to investigate whether AI-supported diabetic retinopathy screening, with a point-of-screening result and face-to-face counselling, increased the proportion of people with true referable diabetic retinopathy who attended the central ophthalmology referral clinic when compared with standard diabetic retinopathy screening with deferred human grading and telephone counselling.

Methods: This two-arm, parallel-group, individually randomised controlled trial was conducted at non-communicable disease or diabetic clinics in 24 hospitals in the Kilimanjaro and Arusha regions of Tanzania. Eligible participants (aged ≥18 years) had diabetes and attended diabetic retinopathy screening. Participants were randomly assigned (1:1) to receive either AI-supported diabetic retinopathy screening with a point-of-screening result and face-to-face counselling (the AI-intervention group) or standard-of-care diabetic retinopathy screening with deferred human retinal image assessment and telephone counselling (the standard-of-care group). Participants had retinal photographs taken of both eyes. Images from the AI-intervention group were submitted to the AI software, and these participants were given their screening result (non-referable, referable, or ungradable) plus face-to-face counselling before leaving the photography room, with a referral to the central ophthalmology clinic if the AI output was referable or ungradable. Participants in the standard-of-care group received their screening result, along with appropriate counselling, by telephone a few weeks later. The primary outcome was attendance at the central ophthalmology clinic within 8 weeks of screening among participants classified by masked UK-certified graders as having true referable diabetic retinopathy. This trial was registered with the ISRCTN Registry, ISRCTN18317152, and is complete.

Findings: Between March 8, 2023, and Aug 31, 2024, 2439 participants were assessed for eligibility and 2378 were enrolled, of whom 1192 were randomly assigned to the AI-intervention group and 1186 to the standard-of-care group. 904 participants had true referable diabetic retinopathy, comprising 437 in the AI-intervention group and 467 in the standard-of-care group. Of the participants in the AI-intervention group, 157 (35·9%) of 437 attended the central ophthalmology clinic within 8 weeks, compared with 85 (18·2%) of 467 in the standard-of-care group, an absolute difference of 17·7 percentage points (95% CI 12·0-23·4; odds ratio 2·52 [95% CI 1·86-3·42]; p<0·0001).

Interpretation: AI-supported screening, with a point-of-screening result and face-to-face counselling, increased the proportion of people with true referable diabetic retinopathy that attended the central ophthalmology clinic after screening compared with the standard of care in Tanzania. Such AI-enabled screening pathways have the potential to improve diabetic retinopathy screening programmes in Africa.

Funding: British Council for the Prevention of Blindness, Christian Blind Mission, and the Sir Halley Stewart Trust.