Ultra-widefield color fundus photography in diabetic retinopathy: from panretinal assessment to multimodal...

Diabetic retinopathy (DR) is the leading cause of vision loss among working-age adults. Early screening and precise staging are crucial for delaying disease progression. Although the traditional Early Treatment of Diabetic Retinopathy Study (ETDRS) 7-field method is the gold standard for grading, it covers only 35% of the retinal area, carrying a risk of missing peripheral lesions. Ultra-widefield color fundus photography (UWF-CFP) overcomes this limitation by capturing up to 200° of the retina in a single image, enabling comprehensive visualization of peripheral ischemia, neovascularization, and peripheral-posterior pole asymmetry lesions. In recent years, combining UWF-CFP with deep learning algorithms has achieved robust performance in automated DR screening, grading, and quantitative vascular analysis. When further integrated with ultra-widefield swept-source optical coherence tomography angiography (UWF SS-OCTA) or multimodal clinical data, these AI-driven methods can improve diagnostic consistency and staging accuracy. Moreover, because the retinal microvasculature mirrors systemic microcirculation, vascular parameters from UWF-CFP have shown correlations with diabetic nephropathy, coronary artery calcification, and stroke risk, highlighting a potential role in non-invasive assessment of systemic complication risks in diabetic patients. UWF-CFP is facilitating a shift in the DR assessment paradigm from ETDRS 7-field to full retinal assessment. Combined with artificial intelligence and multimodal data integration, UWF-CFP has the potential to contribute to a future care model that integrates ocular and systemic risk assessment, which we tentatively term "eye-system collaborative management," though this paradigm remains to be validated in prospective studies.