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Feasibility of an automated AI-based screening tool for diabetic retinopathy at an endocrine outpatient clinic in SA
INTRODUCTION: Diabetic retinopathy (DR) is a worsening global pandemic and a leading cause of blindness. Screening is paramount. In the South African public health sector, screening initiatives have faced significant challenges and leveraging new screening technologies may prove useful. This study a...
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| Language: | English English |
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Division of General Surgery
2025
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| Summary: | INTRODUCTION: Diabetic retinopathy (DR) is a worsening global pandemic and a leading cause of blindness. Screening is paramount. In the South African public health sector, screening initiatives have faced significant challenges and leveraging new screening technologies may prove useful. This study aimed at evaluating the feasibility of an autonomous AI-based diagnostic tool in an endocrine outpatient clinic at Groote Schuur Hospital. METHODS: Patients identified as referable DR (moderate NPDR or worse) by autonomous AI screening, as well as patients with ungradable images, were referred to an ophthalmologist. We assessed the time it took to do screening, number of patients requiring dilation, number of ungradable images and their potential causes, referral burden, and number of patients requiring treatment. RESULTS: A total of 62 patients underwent screening, with a median AI screening time of 11.7 minutes. Of these, 55 (88.7%) required referral to ophthalmology. This included 36 patients (58.1%) with referable DR according to AI grading (of which 19 patients (30.6%) had vision-threatening DR) and 19 (30.6%) with ungradable images despite dilatation. Nine patients (14.5%) were lost to follow-up between AI screening and ophthalmology assessment, and 8 patients (12.9%) required treatment for vision-threatening DR according to ophthalmology human grading. Cataracts were the most important cause for ungradable images. . CONCLUSION: This study showed that screening for diabetic retinopathy using autonomous AI is feasible in terms of time. However, the significant burden of referrals and high number of ungradable images may be problematic within a resource-constrained public healthcare system. |
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