Evolution of diabetic retinopathy imaging: Five decades of technological innovation

Abstract

Screening for diabetic retinopathy (DR) and identifying people with treatable DR is an essential step to prevent vision loss in people with diabetes mellitus. Over the past five decades, retinal imaging technologies have undergone a remarkable transformation, evolving from conventional film-based fundus photography to sophisticated multimodal imaging platforms integrated with artificial intelligence (AI). This review summarizes seven major technological transitions that have reshaped DR imaging: narrow-field to ultra-widefield fundus photography, analog to digital imaging, stereoscopic photography to optical coherence tomography (OCT), fundus fluorescein angiography to OCT angiography, flash photography to scanning laser ophthalmoscopy, manual image grading to automated AI-assisted analysis, and conventional photography to adaptive optics (AO) imaging. Each innovation has improved retinal visualization, image quality, diagnostic precision, and clinical efficiency while expanding opportunities for population-based screening and teleophthalmology. Recent advances in deep learning algorithms have enabled automated detection and severity grading of DR, with performance that matches that of retinal specialists. AO and multimodal imaging provide unprecedented cellular and microvascular resolution. In future, integration of multimodal imaging, explainable AI, cloud-based telemedicine, portable retinal cameras, and home-based monitoring technologies promise earlier detection of vision-threatening disease and personalized patient management. The evolution of DR imaging offers new opportunities to reduce the global burden of diabetes-related blindness through more accessible, accurate, and predictive retinal care.