A deep learning system for the detection of optic disc neovascularization in diabetic retinopathy using optical coherence tomography angiography images

As one of the major complications of diabetic retinopathy (DR), neovascularization of the optic disc (NVD) is a leading cause of visual impairment and blindness. Early identification and timely treatment of NVD are essential to prevent these risks. In this paper, we develop a deep learning (DL) system to identify, quantify, and visualize NVD from optical coherence tomography angiography (OCTA) images. Two datasets of OCTA images were used in this study to develop and evaluate the DL system: (1) 24,576 OCTA images collected from 96 patients with NVD; (2) 15,360 OCTA images from 60 NVD patients with NVD. The task of the DL system involved the detection of the optic disc boundary, the identification of the NVD regions, and the construction and calculation of 3D images for these regions. The DL system achieved promising results in the detection of the optic disc boundary and the identification of NVD regions. The accuracy of the DL system was significantly better than other DL algorithms and comparable to the performance of retina specialists. Furthermore, the DL system could provide a more intuitive 3D image for visualizing the NVD and its blood flow information.