Diagnostic accuracy and clinical performance of deep learning models for grading diabetic retinopathy: a systematic review and meta-analysis
Abstract
Background: Diabetic retinopathy (DR) is a leading cause of preventable visual impairment worldwide, and its precise severity grading is critical for optimizing clinical management. Conventional frameworks, notably the International Clinical Diabetic Retinopathy (ICDR) scale, are often hindered by substantial inter-observer variability and high dependency on specialist expertise. While deep learning (DL) has recently emerged as a transformative approach for automated stratification, a comprehensive synthesis of evidence regarding its diagnostic performance and clinical application remains lacking.
Objective: This systematic review and meta-analysis aimed to comprehensively assess the diagnostic accuracy of fundus image-based deep learning models in the grading of diabetic retinopathy.
Methods: PubMed, Embase, Web of Science, and the Cochrane Library were systematically searched for relevant studies published up to October 28, 2025. Diagnostic accuracy studies utilizing DL algorithms alongside ICDR criteria for diabetic retinopathy grading were included. Literature screening and data extraction were performed independently by two researchers, and the risk of bias was assessed using the QUADAS-2 tool.
Results: A total of 41 studies were included, encompassing various DL architectures and multiple public and private fundus image datasets. In the five-class classification task based on ICDR criteria, the pooled sensitivities of DL-based models varied significantly across…
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Evidence Hub reads this as a meta-analysis, done in people. Its evidence level is 1 of 5 (Highest · pooled evidence). Pooled evidence from several studies sits at the top of the evidence pyramid.
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