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Alam, Minhaj, BS*; Zhang, Yue, MS; Lim, Jennifer I., MD; Chan, Robison.V.P., MD; Yang, Min, PhD; Yao, Xincheng, PhD*,‡

doi: 10.1097/IAE.0000000000002373
Original Study: PDF Only

Purpose: This study aims to characterize quantitative optical coherence tomography angiography (OCTA) features of nonproliferative diabetic retinopathy (NPDR) and to validate them for computer-aided NPDR staging.

Methods: One hundred and twenty 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 the OCTAs were 6 mm × 6 mm macular scans. Six quantitative features, that is, blood vessel tortuosity, blood vascular caliber, vessel perimeter index, blood vessel density, foveal avascular zone area, and foveal avascular zone contour irregularity (FAZ-CI) were derived from each OCTA image. A support vector machine 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 curve was plotted to measure the sensitivity–specificity tradeoff of the classification algorithm.

Results: Among 6 individual OCTA features, blood vessel density shows the best classification accuracies, 93.89% and 90.89% for control versus disease and control versus 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 multiclass classification, support vector machine algorithm achieved 84% accuracy.

Conclusion: Blood vessel density 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.

Quantitative optical coherence tomography angiography features are used for computer-aided classification and objective staging of diabetic retinopathy. Blood vessel density is the most sensitive optical coherence tomography angiography feature, and the temporal-perifoveal retina is the most sensitive region for detecting early onset of nonproliferative diabetic retinopathy.

Departments of *Bioengineering,

Mathematics, Statistics and Computer Sciences, and

Ophthalmology and Visual Sciences, University of Illinois at Chicago, Chicago, Illinois.

Reprint requests: Xincheng Yao, PhD, Department of Bioengineering (MC 563), Department of Ophthalmology and Visual Sciences, University of Illinois at Chicago (UIC), Clinical Sciences North, Suite W103, Room 164D, 820 South Wood Street, Chicago, IL 60612; e-mail:

Supported in part by NIH Grants R01 EY023522, R01 EY024628, and P30 EY001792; by unrestricted grant from Research to Prevent Blindness; by Richard and Loan Hill endowment; and by Marion H. Schenk Chair endowment.

None of the authors has any conflicting interests to disclose.

© 2019 by Ophthalmic Communications Society, Inc.