ABSTRACT

The goal was to discriminate between diabetic retinopathy (DR) and healthy controls (HC) by evaluating Optical coherence tomography angiography (OCTA) images from 3 × 3 mm scans with the assistance of different machine learning models. The OCTA angiography dataset of superficial vascular plexus (SVP), deep vascular plexus (DVP), and retinal vascular network (RVN) were acquired from 19 DR (38 eyes) patients and 25 HC (44 eyes). A discrete wavelet transform was applied to extract texture features from each image. Four machine learning models, including logistic regression (LR), logistic regression regularized with the elastic net penalty (LR-EN), support vector machine (SVM), and the gradient boosting tree named XGBoost, were used to classify wavelet features between groups. The area under the receiver operating characteristics curve (AUC), sensitivity, specificity, and diagnostic accuracy of the classifiers were obtained. The OCTA image dataset included 114 and 132 images from DR and HC subjects, respectively. The LR-EN and LR classification algorithms had the high sensitivity, specificity, and diagnostic accuracy in identifying DR, which may be promising in facilitating the early diagnosis of DR.