Abstract

We aimed to assess the ability of deep learning (DL) and support vector machine (SVM) to detect a nonperfusion area (NPA) caused by retinal vein occlusion (RVO) with optical coherence tomography angiography (OCTA) images. The study included 322 OCTA images (normal: 148; NPA owing to RVO: 174 [128 branch RVO images and 46 central RVO images]). Training to construct the DL model using deep convolutional neural network (DNN) algorithms was provided using OCTA images. The SVM used a scikit-learn library with a radial basis function kernel. The area under the curve (AUC), sensitivity and specificity for detecting an NPA were examined. We compared the diagnostic ability (sensitivity, specificity and average required time) between the DNN, SVM and seven ophthalmologists. Heat maps were generated. With regard to the DNN, the mean AUC, sensitivity, specificity and average required time for distinguishing RVO OCTA images with an NPA from normal OCTA images were 0.986, 93.7%, 97.3% and 176.9 s, respectively. With regard to SVM, the mean AUC, sensitivity, and specificity were 0.880, 79.3%, and 81.1%, respectively. With regard to the seven ophthalmologists, the mean AUC, sensitivity, specificity and average required time were 0.962, 90.8%, 89.2%, and 700.6 s, respectively. The DNN focused on the foveal avascular zone and NPA in heat maps. The performance of the DNN was significantly better than that of SVM in all parameters (p < 0.01, all) and that of the ophthalmologists in AUC and specificity (p < 0.01, all). The combination of DL and OCTA images had high accuracy for the detection of an NPA, and it might be useful in clinical practice and retinal screening.

Highlights

  • Retinal vein occlusion (RVO) is the second most common retinal vascular disease after diabetic retinopathy

  • The present study aimed to assess the ability of image processing technology involving deep learning (DL) and support vector machine (SVM) to detect an nonperfusion area (NPA) owing to RVO using optical coherence tomography angiography (OCTA) images

  • 174 were of eyes with NPA owing to RVO [170 patients; 90 eyes from men and 84 from women; 79 left and 95 right eyes; and 128 eyes with branch retinal vein occlusion (BRVO) and 46 with central retinal vein occlusion (CRVO)], and 148 images were of normal eyes [147 subjects; 75 eyes from men and 73 from women; and 81 left and 67 right eyes]

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Summary

Introduction

Retinal vein occlusion (RVO) is the second most common retinal vascular disease after diabetic retinopathy. The estimated number of RVO patients is 16.4 million [1], with a prevalence of 2.1% in the general population over 40 years of age [2], and risk factors include. Automated detection of retinal nonperfusion area caused by retinal vein occlusion

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