Artificial intelligence driven prognostic models for predicting diabetic retinopathy risk in type 2 diabetes mellitus: A systematic review

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.