Artificial intelligence using deep learning to screen for referable and vision-threatening diabetic retinopathy in Africa: a clinical validation study
2019
Summary Background Radical measures are required to identify and reduce blindness due to diabetes to achieve the Sustainable Development Goals by 2030. Therefore, we evaluated the accuracy of an artificial intelligence (AI) model using deep learning in a population-based
diabetic retinopathyscreening programme in Zambia, a lower-middle-income country. Methods We adopted an ensemble AI model consisting of a combination of two convolutional neural networks (an adapted VGGNet architecture and a
residual neural networkarchitecture) for classifying retinal colour fundus images. We trained our model on 76 370 retinal fundus images from 13 099 patients with diabetes who had participated in the Singapore Integrated
Diabetic RetinopathyProgram, between 2010 and 2013, which has been published previously. In this clinical validation study, we included all patients with a diagnosis of diabetes that attended a mobile screening unit in five urban centres in the Copperbelt province of Zambia from Feb 1 to June 31, 2012. In our model, referable
diabetic retinopathywas defined as moderate non-proliferative
diabetic retinopathyor worse, diabetic
macular oedema, and ungradable images. Vision-threatening
diabetic retinopathycomprised severe non-proliferative and proliferative
diabetic retinopathy. We calculated the area under the curve (AUC), sensitivity, and specificity for referable
diabetic retinopathy, and sensitivities of vision-threatening
diabetic retinopathyand diabetic
macular oedemacompared with the grading by retinal specialists. We did a multivariate analysis for
systemic riskfactors and referable
diabetic retinopathybetween AI and human graders. Findings A total of 4504 retinal fundus images from 3093 eyes of 1574 Zambians with diabetes were prospectively recruited. Referable
diabetic retinopathywas found in 697 (22·5%) eyes, vision-threatening
diabetic retinopathyin 171 (5·5%) eyes, and diabetic
macular oedemain 249 (8·1%) eyes. The AUC of the AI system for referable
diabetic retinopathywas 0·973 (95% CI 0·969–0·978), with corresponding sensitivity of 92·25% (90·10–94·12) and specificity of 89·04% (87·85–90·28). Vision-threatening
diabetic retinopathysensitivity was 99·42% (99·15–99·68) and diabetic
macular oedemasensitivity was 97·19% (96·61–97·77). The AI model and human graders showed similar outcomes in referable
diabetic retinopathyprevalence detection and
systemic riskfactors associations. Both the AI model and human graders identified longer duration of diabetes, higher level of glycated haemoglobin, and increased systolic blood pressure as risk factors associated with referable
diabetic retinopathy. Interpretation An AI system shows clinically acceptable performance in detecting referable
diabetic retinopathy, vision-threatening
diabetic retinopathy, and diabetic
macular oedemain population-based
diabetic retinopathyscreening. This shows the potential application and adoption of such AI technology in an under-resourced African population to reduce the incidence of preventable blindness, even when the model is trained in a different population. Funding National Medical Research Council
Health Service ResearchGrant, Large Collaborative Grant, Ministry of Health, Singapore; the SingHealth Foundation; and the Tanoto Foundation.
Keywords:
-
Correction
-
Source
-
Cite
-
Save
31
References
78
Citations
NaN
KQI