Retinopathy In Africa Research Articles

Artificial intelligence for diabetic retinopathy screening in Africa

Artificial intelligence for diabetic retinopathy screening in Africa

Artificial intelligence using deep learning to screen for referable and vision-threatening diabetic retinopathy in Africa: a clinical validation study

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 retinopathy screening programme in Zambia, a lower-middle-income country. We adopted an ensemble AI model consisting of a combination of two convolutional neural networks (an adapted VGGNet architecture and a residual neural network architecture) 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 Retinopathy Program, 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 retinopathy was defined as moderate non-proliferative diabetic retinopathy or worse, diabetic macular oedema, and ungradable images. Vision-threatening diabetic retinopathy comprised 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 retinopathy and diabetic macular oedema compared with the grading by retinal specialists. We did a multivariate analysis for systemic risk factors and referable diabetic retinopathy between AI and human graders. A total of 4504 retinal fundus images from 3093 eyes of 1574 Zambians with diabetes were prospectively recruited. Referable diabetic retinopathy was found in 697 (22·5%) eyes, vision-threatening diabetic retinopathy in 171 (5·5%) eyes, and diabetic macular oedema in 249 (8·1%) eyes. The AUC of the AI system for referable diabetic retinopathy was 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 retinopathy sensitivity was 99·42% (99·15-99·68) and diabetic macular oedema sensitivity was 97·19% (96·61-97·77). The AI model and human graders showed similar outcomes in referable diabetic retinopathy prevalence detection and systemic risk factors 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. An AI system shows clinically acceptable performance in detecting referable diabetic retinopathy, vision-threatening diabetic retinopathy, and diabetic macular oedema in population-based diabetic retinopathy screening. 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. National Medical Research Council Health Service Research Grant, Large Collaborative Grant, Ministry of Health, Singapore; the SingHealth Foundation; and the Tanoto Foundation.

Artificial Intelligence Using Deep Learning to Screen for Referable and Vision-Threatening Diabetic Retinopathy in Africa

Background: Radical measures are required to identify and reduce blindness due to diabetes to achieve Sustainable Development Goals by 2030. Therefore, we evaluated the accuracy of artificial intelligence (AI) using deep learning in a population based diabetic retinopathy (DR) screening program in Zambia, a lower middle-income country, ranked 159th in terms of gross domestic product. Methods: A total of 4504 retinal fundus images from 3093 eyes of 1574 Zambians with diabetes were prospectively recruited. Referable DR was defined as moderate non-proliferative DR (NPDR) or worse, diabetic macular edema (DME) and ungradable images. Vision-threatening DR (VTDR) included severe NPDR and proliferative DR. We calculated the area under curve (AUC), sensitivity and specificity for referable DR, and sensitivities of VTDR and DME, using an Ensemble convolutional neural network compared to the grading by retinal specialists. A multi-variate analysis was performed for systemic risk factors and referable DR between AI and human graders. Findings: The prevalence of referable DR, VTDR and DME was 22·5%, 5·5% and 8·1% respectively. The AUC of the AI system for referable DR was 0·973, with corresponding sensitivity of 92·25% and specificity of 89·04%. VTDR sensitivity was 99·42% and DME sensitivity was 97·19%. AI model and human graders demonstrated similar outcomes in referable DR prevalence detection and systemic risk factors associations. Both AI model and human graders identified longer duration of diabetes, higher level of HbA1c, and increased systolic blood pressure as risk factors associated with referable DR. Interpretation: An AI system shows clinically acceptable performance in detecting referable DR, VTDR, and DME in population-based DR screening, demonstrating the potential application and adoption of such AI technology in the under-resourced African population to reduce the incidence of preventable blindness. Funding Statement: This project received funding from National Medical Research Council (NMRC) Health Service Research Grant, Ministry of Health (MOH), Singapore (National Health Innovation Center, Innovation to Develop Grant (NHIC-I2D-1409022); SingHealth Foundation Research Grant (SHF/FG648S/2015), and the Tanoto Foundation. The Singapore Diabetic Retinopathy Program (SiDRP) received funding from the MOH, Singapore (grants AIC/RPDD/SIDRP/SERI/FY2013/0018 & AIC/HPD/FY2016/0912). Declaration of Interests: Drs Ting, Lim, Lee, Hsu and Wong are co-inventors of a patent on the deep learning system in this paper; potential conflicts of interests are managed according to institutional policies of the Singapore Health System (SingHealth) and the National University of Singapore (NUS). Ethics Approval Statement: Lewis et al. DR screening program was previously approved by Topical Diseases Research Centre (TDRC), Ndola, Republic of Zambia. Subsequently, this specific study was also approved by the Centralized Institutional Review Board (IRB) of SingHealth, Singapore (protocol number SHF/FG648S/2015) and conducted in accordance with the Declaration of Helsinki.

Diabetic retinopathy in sub-Saharan Africa: meeting the challenges of an emerging epidemic

BackgroundSub-Saharan Africa faces an epidemic of diabetes. Diabetes causes significant morbidity including visual loss from diabetic retinopathy, which is largely preventable. In this resource-poor setting, health systems are poorly organized to deliver chronic care with multiple system involvement. The specific skills and resources needed to manage diabetic retinopathy are scarce. The costs of inaction for individuals, communities and countries are likely to be high.DiscussionScreening for and treatment of diabetic retinopathy have been shown to be effective, and cost-effective, in resource-rich settings. In sub-Saharan Africa, clinical services for diabetes need to be expanded with the provision of effective, integrated care, including case-finding and management of diabetic retinopathy. This should be underpinned by a high quality evidence base accounting for differences in diabetes types, resources, patients and society in Africa. Research must address the epidemiology of diabetic retinopathy in Africa, strategies for disease detection and management with laser treatment, and include health economic analyses. Models of care tailored to the local geographic and social context are most likely to be cost effective, and should draw on experience and expertise from other continents. Research into diabetic retinopathy in Africa can drive the political agenda for service development and enable informed prioritization of available health funding at a national level. Effective interventions need to be implemented in the near future to avert a large burden of visual loss from diabetic retinopathy in the continent.SummaryAn increase in visual loss from diabetic retinopathy is inevitable as the diabetes epidemic emerges in sub-Saharan Africa. This could be minimized by the provision of case-finding and laser treatment, but how to do this most effectively in the regional context is not known. Research into the epidemiology, case-finding and laser treatment of diabetic retinopathy in sub-Saharan Africa will highlight a poorly met need, as well as guide the development of services for that need as it expands.

Diabetic Retinopathy in Africa: Advantages of Screening

Objective: To review the epidemiology of diabetic retinopathy (DR) in Africa and discuss the various methods of screening for the disease. Methods: Review of the peer-reviewed articles in PubMed, Google, and Scirus about DR published from 1985 to 2010. Articles in all languages were considered, provided that the non-English articles included English abstracts. Results: Screening for DR can significantly prevent the loss of vision resulting from long-standing diabetes mellitus. Screening is also financially advantageous for both patients and health care systems. Several screening methods can be employed by primary care providers, who over the past 20 years have become responsible for the treatment and follow-up of patients with diabetes rather than hospitals. An additional concern for screening in African countries is the inefficient referral to ophthalmologists, given their scarce distribution in the African population. Finally, education of both the patient population and caregivers should be included as an essential component of screening programs. Conclusion: As of now, African health care systems are not prepared to deal with the increasing burden of diabetes and its complications. With inappropriate glycemic or blood pressure control, most patients will only seek care once micro- or macrovascular complications have already developed. Given the current difficulty in providing costly therapeutic interventions, a well-planned, cost-effective prevention strategy is required.