Convolutional neural network algorithms in diabetic retinopathy: how far does it go?

Convolutional Neural Networks (CNNs) have rapidly transformed the landscape of medical image analysis, particularly in the detection and management of diabetic retinopathy (DR), a leading cause of irreversible vision loss worldwide. Since their inception in early architectures such as LeNet and AlexNet, CNNs have evolved into advanced designs including VGGNet, Inception, ResNet, U‑Net, DenseNet, EfficientNet, and ConvNeXt, enabling increasingly precise classification, detection, and segmentation of retinal lesions. This systematic review synthesizes current evidence on CNN applications across diverse DR tasks, including disease classification, lesion localization, vessel segmentation, diabetic macular edema detection, ischemia assessment, and disease monitoring. Comparative analyses highlight the performance of CNNs relative to traditional machine learning methods, as well as the benefits of ensemble strategies and customized architectures tailored to multimodal imaging. Despite remarkable progress, real‑world deployment faces persistent challenges related to data quality, computational demands, interpretability, clinical reliability, and ethical considerations. Emerging solutions such as lightweight CNNs, hybrid models, and ensemble learning approaches aim to enhance scalability, efficiency, and clinical integration. The findings reveal both the transformative potential and the limitations of CNNs in DR management, underscoring the need for future research on robust, interpretable, and ethically aligned systems. This review provides ophthalmologists, data scientists, and healthcare practitioners with the latest insights into CNN‑based diabetic retinopathy analysis, fostering the advancement of accessible and clinically reliable diagnostic technologies poised to transform patient care and outcomes.