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Background: Artificial intelligence (AI) appears capable of detecting diabetic retinopathy (DR) with a high degree of accuracy in adults; however, there are few studies in children and young adults. Methods: Children and young adults (3-26 years) with type 1 diabetes mellitus (T1DM) or type 2 diabetes mellitus (T2DM) were screened at the Dhaka BIRDEM-2 hospital, Bangladesh. All gradable fundus images were uploaded to Cybersight AI for interpretation. Two main outcomes were considered at a patient level: 1) Any DR, defined as mild non-proliferative diabetic retinopathy (NPDR or more severe; and 2) Referable DR, defined as moderate NPDR or more severe. Diagnostic test performance comparing Orbis International's Cybersight AI with the reference standard, a fully qualified optometrist certified in DR grading, was assessed using the Matthews correlation coefficient (MCC), area under the receiver operating characteristic curve (AUC-ROC), area under the precision-recall curve (AUC-PR), sensitivity, specificity, positive and negative predictive values. Results: Among 1274 participants (53.1% female, mean age 16.7 years), 19.4% (n = 247) had any DR according to AI. For referable DR, 2.35% (n = 30) were detected by AI. The sensitivity and specificity of AI for any DR were 75.5% (CI 69.7-81.3%) and 91.8% (CI 90.2-93.5%) respectively, and for referable DR, these values were 84.2% (CI 67.8-100%) and 98.9% (CI 98.3%-99.5%). The MCC, AUC-ROC and the AUC-PR for referable DR were 63.4, 91.2 and 76.2% respectively. AI was most successful in accurately classifying younger children with shorter duration of diabetes. Conclusions: Cybersight AI accurately detected any DR and referable DR among children and young adults, despite its algorithms having been trained on adults. The observed high specificity is particularly important to avoid over-referral in low-resource settings. AI may be an effective tool to reduce demands on scarce physician resources for the care of children with diabetes in low-resource settings. [ABSTRACT FROM AUTHOR]

Objectives: Diabetic retinopathy (DR) is the most common microvascular complications seen in children and adolescents with type 1 diabetes. The aim of this study was to evaluate the prevalence of retinopathy and its association with other risk factors in young people with type 1 diabetes. Methods: This study was a cross‐sectional study, which was done as part of the ongoing complication assessment in the paediatric diabetes clinic in BIRDEM (Bangladesh Institute of Research and Rehabilitation of Diabetes Endocrine and Metabolic Disorders), a tertiary care hospital. Children, adolescents and young adults with type 1 diabetes who were having diabetes duration >2 years were included in this study. Retinopathy was detected using fundal photography, and grading was done by National Screening Committee of UK by trained ophthalmologists. Results: Diabetic retinopathy was observed in 44 (6.6%) patients. Majority (95.4%) of them had early diabetic retinopathy in the form of mild NPDR (nonproliferative diabetic retinopathy) (R1). Patients with retinopathy had higher HbA1c 9.6[8.4‐12.3] vs 9.1 [7.9‐10.8] (P =.013), longer duration of diabetes 7.6 [5.5‐10.7] vs 6.0 [4.5‐8.2] years (P =.001) and were older 21.5 [18.0‐23.0] vs 18 [16.0‐21.0] years (P =.0001) compared with those without retinopathy. On multivariate regression analysis, higher age and median HbA1c were significantly associated with DR. Conclusions: Higher HbA1c was the only modifiable risk factor for development of DR in our study population. Early detection of DR with improvement of glycaemic control may reduce the risk of progression of severe stages of the disease. [ABSTRACT FROM AUTHOR]