Diabetic retinopathy (DR) is a leading cause of blindness worldwide, requiring early detection and accurate grading for effective intervention. Advances in artificial intelligence (AI), computer vision, machine learning, and deep learning (DL) have enabled automated detection and classification of DR through various imaging modalities. This review comprehensively evaluates 91 studies employing AI-based methods in the detection and classification of DR using fundus color photography, optical coherence tomography (OCT), OCT-angiography (OCTA), and fundus fluorescein angiography, providing a holistic understanding of their strengths, challenges, and limitations. Additionally, this review compares the characteristics of 23 public datasets for DR. Across modalities, DL approaches generally outperform traditional methods. Among the studies reviewed, 81% utilized fundus images, followed by 9% using OCT, 6% using OCTA, and 2% incorporating multiple modalities. Regarding classification tasks, 62% used AI for multi-way classification, 28% for binary classification, and 10% incorporated both. The paper concludes with future directions, including explainable AI frameworks, multimodal data integration, and suggested protocols to integrate into existing healthcare workflows.
Keywords: Artificial intelligence; Computer vision; Deep learning; Diabetic retinopathy; Machine learning; Retinal imaging modalities.
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