| Autor: |
Whitestone, Noelle, Nkurikiye, John, Patnaik, Jennifer L, Jaccard, Nicolas, Lanouette, Gabriella, Cherwek, David H, Congdon, Nathan, Mathenge, Wanjiku |
| Zdroj: |
British Journal of Ophthalmology; 2024, Vol. 108 Issue: 6 p840-845, 6p |
| Abstrakt: |
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. |
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