Abstract / Summary
Introduction: Diabetic retinopathy remains a leading cause of preventable blindness among individuals with type 2 diabetes mellitus and is driven by the increasing global burden of diabetes. Early identification of individuals at high risk is essential for timely intervention. In recent years, artificial intelligence, including machine learning and deep learning, has emerged as a promising approach for prognostic modelling. However, evidence on artificial intelligence-driven models for predicting diabetic retinopathy risk remains fragmented. This systematic review aims to synthesise current evidence on artificial intelligence-driven prognostic models for predicting the risk of diabetic retinopathy among individuals with type 2 diabetes mellitus, focusing on model characteristics, predictor variables, performance, and validation strategies.
Materials and methods: A comprehensive search of PubMed, Scopus, Web of Science, and ScienceDirect was performed for studies published between January 2016 and December 2025. Eligible studies included those applying artificial intelligence-based models for diabetic retinopathy risk prediction in adult populations with type 2 diabetes mellitus and reporting model performance metrics. Data were extracted and synthesised narratively, and methodological quality was assessed using the Newcastle-Ottawa Scale.
Results: A total of 1040 records were identified, with eight studies included after screening. A wide range of artificial intelligence algorithms was applied, with ensemble models such as extreme gradient boosting and random forest demonstrating superior performance. Key predictors consistently included glycated haemoglobin levels, duration of diabetes, blood pressure, lipid profile, and renal function markers, alongside emerging metabolomic biomarkers. Model performance ranged from moderate to excellent, with area under the receiver operating characteristic curve values between 0.68 and 0.97. Most studies employed internal validation techniques such as cross-validation or training and testing data splits, while external validation was largely absent. Overall methodological quality was high, although variability in study design, predictor selection, and reporting was observed.
Conclusion: Artificial intelligence-driven prognostic models show substantial potential in predicting diabetic retinopathy risk among individuals with type 2 diabetes mellitus, particularly through the integration of clinical and highdimensional data. Despite promising predictive performance, limitations in external validation, heterogeneity, and reporting of clinical utility hinder translation into practice. Future research should prioritise external validation, standardised reporting frameworks, and clinically interpretable models to enhance applicability in real-world healthcare settings.