A convolutional neural network for screening and staging of diabetic retinopathy based on wide-field optical coherence tomography angiography

Bowen Dong; Xiangning Wang; Guogang Cao; Lei Gao; Fengxian Du; Qiang Wu; Cuixia Dai

doi:10.1117/12.2601354

9 October 2021 A convolutional neural network for screening and staging of diabetic retinopathy based on wide-field optical coherence tomography angiography

Bowen Dong, Xiangning Wang, Guogang Cao, Lei Gao, Fengxian Du, Qiang Wu, Cuixia Dai

Author Affiliations +

Proceedings Volume 11900, Optics in Health Care and Biomedical Optics XI; 1190028 (2021) https://doi.org/10.1117/12.2601354
Event: SPIE/COS Photonics Asia, 2021, Nantong, JS, China

Conference Poster

Abstract

Diabetic retinopathy (DR) accounts for accumulated damage to retinal blood vessels which can lead to blindness if it is not detected in its early stage. Optical coherence tomography angiography (OCTA) provides noninvasive and dye-free method to assess 3D retinal and choroid circulations which has been used to evaluate DR ever since it was proposed. In this study, widefield OCTA (WF-OCTA) images were provided by the swept-source optical coherence tomography (SS-OCT) with a 12mm×12mm single scan centered on the fovea and a convolutional neural network (CNN) model was proposed to extract small lesions present in images for the early detection of DR. The proposed model achieved a classification accuracy of 95%, sensitivity of 97.12% and specificity of 87.90% in detecting DR. The accuracy of the model for DR staging is 85.74%, which is higher than that of the Vgg16 by 5.76% and the Inception-V3 by 4.49%. This work demonstrated reproducible and consistent detection results with high sensitivity and specificity.

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Bowen Dong, Xiangning Wang, Guogang Cao, Lei Gao, Fengxian Du, Qiang Wu, and Cuixia Dai "A convolutional neural network for screening and staging of diabetic retinopathy based on wide-field optical coherence tomography angiography", Proc. SPIE 11900, Optics in Health Care and Biomedical Optics XI, 1190028 (9 October 2021); https://doi.org/10.1117/12.2601354

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