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Validation of a Deep Learning System for the Detection of Diabetic Retinopathy in Indigenous Australians

Overview
Journal Br J Ophthalmol
Specialty Ophthalmology
Date 2023 Feb 6
PMID 36746615
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Abstract

Background/aims: Deep 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.

Methods: We 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.

Results: For 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).

Conclusion: The 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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References
1.
Krause J, Gulshan V, Rahimy E, Karth P, Widner K, Corrado G . Grader Variability and the Importance of Reference Standards for Evaluating Machine Learning Models for Diabetic Retinopathy. Ophthalmology. 2018; 125(8):1264-1272. DOI: 10.1016/j.ophtha.2018.01.034. View

2.
Mathenge W, Whitestone N, Nkurikiye J, Patnaik J, Piyasena P, Uwaliraye P . Impact of Artificial Intelligence Assessment of Diabetic Retinopathy on Referral Service Uptake in a Low-Resource Setting: The RAIDERS Randomized Trial. Ophthalmol Sci. 2022; 2(4):100168. PMC: 9754978. DOI: 10.1016/j.xops.2022.100168. View

3.
Ting D, Peng L, Varadarajan A, Keane P, Burlina P, Chiang M . Deep learning in ophthalmology: The technical and clinical considerations. Prog Retin Eye Res. 2019; 72:100759. DOI: 10.1016/j.preteyeres.2019.04.003. View

4.
Ipp E, Liljenquist D, Bode B, Shah V, Silverstein S, Regillo C . Pivotal Evaluation of an Artificial Intelligence System for Autonomous Detection of Referrable and Vision-Threatening Diabetic Retinopathy. JAMA Netw Open. 2021; 4(11):e2134254. PMC: 8593763. DOI: 10.1001/jamanetworkopen.2021.34254. View

5.
Simo-Servat O, Hernandez C, Simo R . Diabetic Retinopathy in the Context of Patients with Diabetes. Ophthalmic Res. 2019; 62(4):211-217. DOI: 10.1159/000499541. View