Feasibility and acceptance of artificial intelligence-based diabetic retinopathy screening in Rwanda

Abstract

BackgroundEvidence on the practical application of artificial intelligence (AI)-based diabetic retinopathy (DR) screening is needed.MethodsConsented participants were screened for DR using retinal imaging with AI interpretation from March 2021 to June 2021 at four diabetes clinics in Rwanda. Additionally, images were graded by a UK National Health System-certified retinal image grader. DR grades based on the International Classification of Diabetic Retinopathy with a grade of 2.0 or higher were considered referable. The AI system was designed to detect optic nerve and macular anomalies outside of DR. A vertical cup to disc ratio of 0.7 and higher and/or macular anomalies recognised at a cut-off of 60% and higher were also considered referable by AI.ResultsAmong 827 participants (59.6% women (n=493)) screened by AI, 33.2% (n=275) were referred for follow-up. Satisfaction with AI screening was high (99.5%, n=823), and 63.7% of participants (n=527) preferred AI over human grading. Compared with human grading, the sensitivity of the AI for referable DR was 92% (95% CI 0.863%, 0.968%), with a specificity of 85% (95% CI 0.751%, 0.882%). Of the participants referred by AI: 88 (32.0%) were for DR only, 109 (39.6%) for DR and an anomaly, 65 (23.6%) for an anomaly only and 13 (4.73%) for other reasons. Adherence to referrals was highest for those referred for DR at 53.4%.ConclusionDR screening using AI led to accurate referrals from diabetes clinics in Rwanda and high rates of participant satisfaction, suggesting AI screening for DR is practical and acceptable.

Comparison of 21 artificial intelligence algorithms in automated diabetic retinopathy screening using handheld fundus camera

“…The primary starting point for implementation of automated screening systems into clinical use could be sorting out the fundus images with no DR or other pathologies from the ones with any DR. According to our results, this would at least halve the need for human grading and hence reduce the cost and time used for analysis since most of the patients, almost 60%, had no DR. Usage of AI systems have indeed been demonstrated to lower cost by at least partially replacing human graders, improving diagnostic accuracy and increasing patient access to DR screening [ 8 , 12 ]. Automated DR detection algorithms have several advantages over human-based screening; algorithms do not get tired and can grade thousands of fundus images a day.…”

“…However, resources for nationwide screening programs are scarce in many countries. In rural areas and low income countries, the need to travel vast distances and the lack of retinal cameras, trained healthcare professionals and ophthalmologists are important barriers to the clinical implementation of DR screening [ 11 , 12 ]. Current screening systems also rely greatly on human graders, a resource both costly and in limited supply.…”

Advancing Glaucoma Care: Integrating Artificial Intelligence in Diagnosis, Management, and Progression Detection

“…Compared to medical professionals, it achieved an accuracy of 83.4% in identifying glaucomatous damage. Orbis International provides free access to an AI tool called Cybersight AI to eye care professionals in low-and middle-income countries [33]. This open access tool can detect diabetic retinopathy, glaucoma, and macular disease on fundus images.…”

“…This open access tool can detect diabetic retinopathy, glaucoma, and macular disease on fundus images. At clinics in Rwanda, screening with this device led to accurate referrals for diabetic retinopathy and high rates of patient satisfaction, though more research is needed on diagnostic accuracy for glaucoma [33].…”

Artificial Intelligence Improves Patient Follow-Up in a Diabetic Retinopathy Screening Program

“…4 During the past two decades, progress in teleophthalmology-based DR screening, consisting of remote, asynchronous interpretation of fundus photographs by experienced human readers, has offered an avenue to overcome some of these barriers. [5][6][7][8][9] More recently, artificial intelligence (AI)-based technologies for DR screening have the potential to further address this challenge by offering expert-level diagnostic ability at a low marginal cost. [10][11][12][13] The ultimate public health impact of a screening program, though, is determined by whether patients identified as having referrable disease during screening subsequently receive appropriate follow-up care.…”