Detection of diabetic retinopathy in its early stages using textural features of optical coherence tomography angiography

Abstract

The aim of this study is to detect whether the quantitative textural features of optical coherence tomography angiography (OCTA) images can be used to detect the eyes in the early stage of diabetic retinopathy (DR) from eyes with diabetes and no DR (NDR). Textural features including fractal dimension, contrast, correlation, entropy, energy, and homogeneity were calculated from the OCTA images. The Student’s [Formula: see text]-test was performed to identify the textural features that can be able to detect DR in the early stage. The area under the receiver operating characteristic (AUROC) curves, sensitivity, and specificity were calculated between the study groups. Our results indicated that the fractal dimension in ICP and SVP and the correlation in SVC showed the statistical significance between mild NPDR patients and NDR patients. The ROC analysis results showed that the AUROC of the fractal dimension in ICP was 0.736 with 0.773 sensitivity and 0.700 specificity. The cutoff point in ICP was 2.616. The OCTA-based fractal dimension was able to discriminate diabetic eyes with early retinopathy from healthy and NDR with higher sensitivity and specificity. The OCTA-based correlation showed the power to differentiate the mild NPDR eyes from the normal healthy and diabetic eyes. These results suggest that texture-based features of OCTA have the potential to assist in the assessment of therapeutic interventions to prevent early DR in diabetic subjects.