The artificial intelligence driven on the development of diabetic retinopathy prognostic scoring tool among type 2 diabetes mellitus patients: A review

Diabetic retinopathy, a major microvascular complication of type 2 diabetes mellitus, remains a leading cause of preventable blindness worldwide. Early identification of individuals at high risk is essential, yet conventional screening systems are limited by workforce shortages and delayed detection. Artificial intelligence, particularly machine learning, offers substantial potential to support prognostic scoring tools capable of predicting the development of diabetic retinopathy. This review summarises current evidence on AI-driven prognostic models for diabetic retinopathy among adults with type 2 diabetes mellitus.

A comprehensive PubMed search using Medical Subject Headings and free-text terms related to "Diabetic Retinopathy," "Type 2 Diabetes Mellitus," "Artificial Intelligence," "Machine Learning," and "Prognostic Model" was conducted. Original studies involving adults with T2DM that developed or evaluated AIor ML-based prognostic or risk-scoring tools for DR were included. Extracted data included study design, sample size, artificial intelligence methods, predictors, and model performance, and were synthesised narratively.

From 759 records, five studies met the inclusion criteria. Extreme Gradient Boosting consistently demonstrated the highest predictive performance, with area under the curve values between 0.803 and 0.966. Support Vector Machine also performed well in smaller cohorts. Key predictors across studies included HbA1c, duration of diabetes, renal function markers, blood pressure, lipid profile, and body mass index.

AI-driven prognostic tools show strong potential to enhance early diabetic retinopathy risk prediction. However, broader external validation and population-specific calibration are needed before routine clinical adoption.