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Diabetic retinopathy (DR), a microvascular complication of diabetes, is an important cause of preventable blindness and can cause a significant reduction in the quality of life of working-age adults. Sri Lanka has one of the highest prevalences of diabetes globally, and like many low- and middle-income countries, faces significant barriers to DR screening. Easy-to-use, relatively cheap handheld retinal fundus imaging devices with automatic interpretation using artificial intelligence (AI) presents a new avenue for DR screening by non-specialist healthcare workers. This study piloted the use of one such device by non-specialist healthcare workers in a low-resource setting and aimed to evaluate image gradability and the diagnostic test accuracy (DTA) of the AI interpretation in this context. Adults with and without diabetes were recruited from a research clinic. Non-specialist healthcare workers received brief training to capture macular- and optic-disc-centred retinal images using an AI-assisted handheld retinal imaging camera (Remidio FOP NM-10), before and after mydriasis. The device generated reports identifying referrable DR. Participants also had visual acuity assessed and underwent slit lamp biomicroscopy by an ophthalmologist. We evaluated image gradability of the captured images pre- and post-dilation, as well as the DTA of the AI reports using the ophthalmologist’s findings as the gold-standard. Retinal images were captured on both eyes of 119 participants, including 49 with diabetes. The proportion of participants that had ungradable images of at least one eye prior to mydriasis was 46.2%, and this fell to 15.1% after mydriasis. A higher percentage of older individuals (59% of those aged 55–64 years and 70% of those aged 65–74 years), 85% of those with cataracts and 58% of those with poor visual acuity had at least one eye with ungradable images pre-mydriasis, with these percentages reducing by 40–70% with mydriasis. The sensitivity and specificity for referral of patients compared to the gold-standard was 73.3% and 79.8% respectively, with image gradability a major driver of misclassification. The AI-assisted retinal imaging camera used by non-specialist healthcare workers after minimal training, in a low-resource setting shows promise as a feasible tool to identify DR in dilated eyes.
Wijemunige et al. (Wed,) studied this question.