ArticleThe British journal of ophthalmology2024
Validation of a deep learning system for the detection of diabetic retinopathy in Indigenous Australians.
Article in The British journal of ophthalmology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 18 citations in OpenAlex.
- Practical lessons in the global scaling of clinical AI: from one hospital to over a million patients screened.Nature medicine · 2026Article
- Reclaiming Data, Restoring Health: The Indigenous Biomedical Data Science Renaissance.Annual review of biomedical data science · 2026Review
- Systematic review and meta-analysis of regulator-approved deep learning systems for fundus diabetic retinopathy detections.NPJ digital medicine · 2025Article
- A Deep Learning Segmentation Model for Detection of Active Proliferative Diabetic Retinopathy.Ophthalmology and therapy · 2025Article
- Implementation of A New, Mobile Diabetic Retinopathy Screening Model Incorporating Artificial Intelligence in Remote Western Australia.The Australian journal of rural health · 2025Article
- Artificial intelligence is going to transform the field of endocrinology: an overview.Frontiers in endocrinology · 2025Article
- Economic evaluations of eye care services for Indigenous populations in high-income countries: a scoping review.International journal for equity in health · 2024Article
- Foundation models in ophthalmology.The British journal of ophthalmology · 2024Review
- A comparative evaluation of deep learning approaches for ophthalmology.Scientific reports · 2024Review
- A Review on Electronic Health Record Text-Mining for Biomedical Name Entity Recognition in Healthcare Domain.Healthcare (Basel, Switzerland) · 2023Review
- Accuracy of Integrated Artificial Intelligence Grading Using Handheld Retinal Imaging in a Community Diabetic Eye Screening Program.Ophthalmology scienceArticle
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Authors and funding
7 authors at 3 institutions in 3 countries.
Funding
Abstract
BACKGROUND/
aimsDeep learning systems (DLSs) for diabetic retinopathy (DR) detection show promising results but can underperform in racial and ethnic minority groups, therefore external validation within these populations is critical for health equity. This study evaluates the performance of a DLS for DR detection among Indigenous Australians, an understudied ethnic group who suffer disproportionately from DR-related blindness.
methodsWe performed a retrospective external validation study comparing the performance of a DLS against a retinal specialist for the detection of more-than-mild DR (mtmDR), vision-threatening DR (vtDR) and all-cause referable DR. The validation set consisted of 1682 consecutive, single-field, macula-centred retinal photographs from 864 patients with diabetes (mean age 54.9 years, 52.4% women) at an Indigenous primary care service in Perth, Australia. Three-person adjudication by a panel of specialists served as the reference standard.
resultsFor mtmDR detection, sensitivity of the DLS was superior to the retina specialist (98.0% (95% CI, 96.5 to 99.4) vs 87.1% (95% CI, 83.6 to 90.6), McNemar's test p<0.001) with a small reduction in specificity (95.1% (95% CI, 93.6 to 96.4) vs 97.0% (95% CI, 95.9 to 98.0), p=0.006). For vtDR, the DLS's sensitivity was again superior to the human grader (96.2% (95% CI, 93.4 to 98.6) vs 84.4% (95% CI, 79.7 to 89.2), p<0.001) with a slight drop in specificity (95.8% (95% CI, 94.6 to 96.9) vs 97.8% (95% CI, 96.9 to 98.6), p=0.002). For all-cause referable DR, there was a substantial increase in sensitivity (93.7% (95% CI, 91.8 to 95.5) vs 74.4% (95% CI, 71.1 to 77.5), p<0.001) and a smaller reduction in specificity (91.7% (95% CI, 90.0 to 93.3) vs 96.3% (95% CI, 95.2 to 97.4), p<0.001).
conclusionThe DLS showed improved sensitivity and similar specificity compared with a retina specialist for DR detection. This demonstrates its potential to support DR screening among Indigenous Australians, an underserved population with a high burden of diabetic eye disease.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.