Prediction of OCT contours of short-term response to anti-VEGF treatment for diabetic macular edema using generative adversarial networks

Diabetic macular edema (DME) stands as a leading cause for vision loss among the working-age population. Antivascular endothelial growth factor (VEGF) agents are currently recognized as the first-line treatment. However, a significant portion of patients remain insensitive to anti-VEGF, resulting in sustained visual impairment. Therefore, it's imperative to predict prognosis and formulate personalized therapeutic regimens. Generative adversarial networks (GANs) have demonstrated remarkably in forecasting prognosis of diseases, yet their performance is still constrained by the limited availability of real-world data and suboptimal image quality, which subsequently impacts the model's outputs. We endeavor to employ preoperative images along with postoperative OCT contours annotated and extracted via LabelMe and OpenCV to train the model in generating postoperative contours of critical OCT structures instead of previous whole retinal morphology, considerably alleviating the difficulty of output phase and diminishing the requisite quantity of training datasets. Our study reveals that the GAN could serve as an auxiliary instrument for ophthalmologists in determining the prognosis of individuals and screening patients with poor responses to anti-VEGF therapy.