Unfolding the diagnostic pipeline of diabetic retinopathy with artificial intelligence: A systematic review.

Abstract

Diabetic retinopathy (DR) is a leading cause of vision impairment globally, necessitating early and accurate detection through effective screening methods. We focus on the integration of artificial intelligence (AI) techniques in automating and enhancing DR diagnosis. Timely detection and classification of DR severity are critical for patient management and intervention. AI-driven DR classification frameworks typically consist of sequential stages: image preprocessing, optic disc (OD) localization and removal, blood vessel segmentation, feature extraction, and classification of DR severity. In the proposed and implemented model, each of these phases was systematically addressed to ensure improved performance. The implementation demonstrated superior accuracy, achieving 98.02 % on the widely used MESSIDOR dataset. The pipeline incorporated effective preprocessing to enhance image quality, accurate OD localization and exclusion to avoid false detections, followed by precise vessel segmentation. Extracted features were then used to train deep learning models for DR severity classification. Comparative analysis with existing methods executed on the same dataset revealed that proposed model outperformed other state-of-the-art techniques in terms of classification accuracy and robustness. Ww outline the recent progress in AI-based DR screening, highlighting the significance of each diagnostic phase and their role in improving overall performance. By evaluating multiple approaches and benchmarking them against established dataset, the study emphasizes the transformative role of AI in DR diagnosis. Despite current challenges, AI holds substantial promise in clinical application, offering scalable, accurate, and efficient DR screening solutions that may significantly reduce the risk of blindness in diabetic patients.