Quantitative OCT angiography features for objective classification and staging of diabetic retinopathy - Alam, Minhaj (Retina, 2018)
PURPOSE: This study aims to characterize quantitative optical-coherence-tomography-angiography (OCTA) features of non-proliferative diabetic-retinopathy (NPDR), and to validate them for computer-aided NPDR staging. METHODS: 120 OCTA images from 60 NPDR (mild, moderate and severe stages) patients and 40 images from 20 control subjects were used for this study conducted in a tertiary, subspecialty, academic practice. Both eyes were photographed and all of the OCTAs were 6 mm × 6 mm macular scans. Six quantitative features, i.e., blood vessel tortuosity (BVT), blood vascular caliber (BVC), vessel perimeter index (VPI), blood vessel density (BVD), foveal avascular zone (FAZ) area (FAZ-A), and FAZ contour irregularity (FAZ-CI) were derived from each OCTA image. A support vector machine (SVM) classification model was trained and tested for computer-aided classification of NPDR stages. Sensitivity, specificity and accuracy were used as performance-metrics of computer-aided classification and receiver-operation-characteristics (ROC) curve was plotted to measure the sensitivity-specificity tradeoff of the classification algorithm. RESULTS: Among six individual OCTA features, BVD shows the best classification accuracies, 93.89% and 90.89% for control vs. disease and control vs. mild-NPDR, respectively. Combined-feature classification achieved improved accuracies, 94.41% and 92.96% respectively. Moreover, the temporal-perifoveal region was the most-sensitive region for early detection of DR. For multi-class classification, SVM algorithm achieved 84% accuracy. CONCLUSION: BVD was observed as the most-sensitive feature and temporal-perifoveal region was the most-sensitive region for early detection of DR. Quantitative OCTA analysis enabled computer-aided identification and staging of NPDR.