Artificial intelligence for diabetic retinopathy screening in Africa
Authors: Wanjiku Ciku Mathenge
Journal: The Lancet Digital Health (2019)

Blindness from diabetic retinopathy has become another addition to the myriad of demands on eye health systems in low-resource countries. This is driven by the rising number of people living with diabetes all over the world, with the largest increases projected in Asia and Africa.1Flinders P Alexander M Patel JV Diabetes in sub-Saharan Africa: an economic and public health emergency.Int J Clin Pract. 2013; 67: 1074-1075Crossref PubMed Scopus (1) Google Scholar Despite screening for diabetic retinopathy in the population with diabetes having been shown to be an effective public health intervention in reducing progression to vision loss, low-resource settings often lack the infrastructure and trained personnel to set up diabetic retinopathy screening programmes.2Poore S Foster A Zondervan M Blanchet K Planning and developing services for diabetic retinopathy in sub-Saharan Africa.Int J Health Policy Manag. 2015; 4: 19-28Crossref PubMed Scopus (21) Google Scholar Often, the same individual will be expected to screen, follow up, and treat patients with diabetic retinopathy as well as run other eye services. Although tele-ophthalmology has been attempted in Africa, the images taken still needed to be sent to a reading centre, meaning patients had to be recalled at a later date to receive their results.3Matimba A Woodward R Tambo E Ramsay M Gwanzura L Guramatunhu S Tele-ophthalmology: opportunities for improving diabetes eye care in resource- and specialist-limited Sub-Saharan African countries.J Telemed Telecare. 2016; 22: 311Crossref PubMed Scopus (25) Google Scholar Other screening programmes that provide patients with instant feedback have involved taking an individual who normally provides key services at eye departments and training them to become a grader either by dilating pupils and doing fundoscopy or by reading digital images when cameras are available.4Webb EM Rheeder P Roux P Screening in primary care for diabetic retinopathy, maculopathy and visual loss in South Africa.Ophthalmologica. 2016; 235: 141-149Crossref PubMed Scopus (11) Google Scholar Taking such a skilled individual from a setup already struggling with shortages of trained eye workers adds further strain to the system.5Burgess PI Msukwa G Beare NA Diabetic retinopathy in sub-Saharan Africa: meeting the challenges of an emerging epidemic.BMC Med. 2013; 11: 157Crossref PubMed Scopus (39) Google Scholar Such screening programmes are therefore often sporadic, either because the screener cannot be available to provide continuous service or because often the one available camera has to be moved between the screening site and the base hospital. In The Lancet Digital Health, Vallentina Bellemo and colleagues describe the use of artificial intelligence (AI) as a possible solution to mitigate these screening issues and reduce blindness from diabetic retinopathy.6Bellemo V Lim SW Lim G et al.Artificial intelligence using deep learning to screen for referable and vision-threatening diabetic retinopathy in Africa: a validation study.Lancet Digital Health. 2019; 1: e35-e44Summary Full Text Full Text PDF Scopus (121) Google Scholar Using AI for digital retinopathy screening will allow non-clinicians to be trained on retinal imaging, obtaining interpretation of the images within minutes and thus giving patients instant feedback. In Africa, this task shifting to non-clinical staff will spare ophthalmologists valuable time and also allow patients, who often travel for long distances to screening centres, to return home or progress to eye clinics with an answer on their diabetic retinopathy status without waiting for feedback from reading centres, which often necessitates an extra journey for them. The Article describes a tested methodology using convolutional neural networks and its effectiveness in reading images from African patients. Although Bellemo and colleagues' final ensemble model had not been previously trained on African retinas,7Ting DSW Cheung CY-L Lim G et al.Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes.JAMA. 2017; 318: 2211-2223Crossref PubMed Scopus (999) Google Scholar which are more pigmented than Asian or white retinas,8Bourne RR Ethnicity and ocular imaging.Eye (Lond). 2011; 25: 297-300Crossref PubMed Scopus (18) Google Scholar the results they report are promising, showing the AI model and human graders to have similar outcomes in detection of referable diabetic retinopathy and systemic risk factor associations. The area under the curve 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). These results are in keeping with the only other published work involving African populations and AI for diabetic retinopathy, which was done in Nakuru, Kenya.9Hansen MB Abràmoff MD Folk JC Mathenge W Bastawrous A Peto T Results of automated retinal image analysis for detection of diabetic retinopathy from the Nakuru study, Kenya.PLoS One. 2015; 10: e0139148Crossref PubMed Scopus (39) Google Scholar Bellemo and colleagues' Article helps to fill the gaps in reporting and application of AI, not just in health but also in other scientific fields in Africa. Additionally, there is a need to develop AI products that have been adequately tested and trained on African populations to ensure discrimination is not built into AI models for diabetic retinopathy screening, which would bias how the algorithms respond to retinal fundus images from an African population.10Bellamy RKE Dey K Hind M et al.AI Fairness 360: an extensible toolkit for detecting, understanding, and mitigating unwanted algorithmic bias.arXiv. 2018; (published online Oct 3. 1810.01943v1 (preprint))Google Scholar This Article should stimulate further evidence generation not just to test the accuracy of AI models but also to encourage operational research on the practical applications of this technology, such as the cost of cameras, availability of wireless connectivity, and the cost of AI software packages and how to make them widely available across Africa. The Article points out the need for standardisation of diabetic retinopathy classification systems. Indeed, use of AI could also stimulate clarity on what should be classified as referable diabetic retinopathy and what is considered a good response to treatment. It might also lead to standardisation of cameras. In any case, this Article confirms that AI solutions can be used to grade diabetic retinopathy in Africa. Thought must now be given to the development of AI expertise in Africa, including the development of legal frameworks covering issues such as data protection. Even more important is to ensure that post-screening services for those found positive for diabetic retinopathy attract the necessary investment because treatments such as photocoagulation and antivascular endothelial growth factor injections are either unavailable or unaffordable in many parts of Africa. I declare no competing interests. Artificial intelligence using deep learning to screen for referable and vision-threatening diabetic retinopathy in Africa: a clinical validation studyAn 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. Full-Text PDF Open Access