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1. Building capacity for diabetic eye screening and management in low-middle income countries : a mixed-methods approach

Author

Curran, Katie, Congdon, Nathan, Peto, Tunde, and Lohfeld, Lynne

Subjects
617.7, Diabetes, eye screening, diabetes eye screening program
Abstract

Worldwide, the burden of diabetes mellitus (DM) and its complications are becoming a major public health concern, particularly in low-middle income countries (LMICs), where 80% of people with DM (PwDM) reside. The main sight-threatening complication of DM is diabetic retinopathy (DR). Early detection through diabetic eye screening (DESPs) is fundamental to reduce the risk of avoidable DR-related blindness or visual impairment. Since the introduction of systematic DESPs in high-income countries such as the UK, DR is no longer the leading cause of blindness among working age adults. The main aim of this thesis is to enable capacity building for DESPs in LMICs, focusing on two low-resource settings, Vietnam and Bangladesh. Identifying gaps in national level DR policies and planning in LMICs is crucial to inform and enact DR policies. A mixed methods approach (quantitative and qualitative analyses) was used to evaluate the feasibility of DESPs in Bangladesh and Vietnam, and enabling capacity building in both settings was key. Gaps in DR policy processes and DESP implementation in LMICs exist, especially in low-income countries. To build capacity for DESPs in Vietnam nurses were trained to perform DR screening and grading; however, their diagnostic test accuracy was low, demanding further exploration and evaluation. The think-aloud study assessed the decision-making processes of novice and expert DR graders to improve DR training protocols in Vietnam. Compared to novices, expert graders were more likely to systematically grader fundus images and use all available grading tools, such as the red-free filter to view more discrete signs of DR. Furthermore, high costs and distance to hospitals were two main barriers to DR screening and treatment uptake in Bangladesh and Vietnam; therefore, intervention strategies to improve uptake are necessary. To ensure DESPs are sustainable, fully integrated and universally accessible, careful planning, development and evaluation are required.

Published
2021

2. CHILDSTAR: CHIldren Living With Diabetes See and Thrive with AI Review.

Author

Curran, Katie, Whitestone, Noelle, Zabeen, Bedowra, Ahmed, Munir, Husain, Lutful, Alauddin, Mohammed, Hossain, Mohammad Awlad, Patnaik, Jennifer L, Lanoutee, Gabriella, Cherwek, David Hunter, Congdon, Nathan, Peto, Tunde, and Jaccard, Nicolas

Subjects
*STATISTICS, *PREDICTIVE tests, *CONFIDENCE intervals, *MULTIVARIATE analysis, *MULTIPLE regression analysis, *TYPE 1 diabetes, *ARTIFICIAL intelligence, *MEDICAL screening, *MACHINE learning, *TYPE 2 diabetes, *RISK assessment, *DESCRIPTIVE statistics, *DIABETIC retinopathy, *RECEIVER operating characteristic curves, *SENSITIVITY & specificity (Statistics), *DATA analysis software, *DISEASE risk factors, *DISEASE complications, *CHILDREN, *ADOLESCENCE
Abstract

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]

Published
2023
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