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

PURPOSE: This study sought to assess the predictive performance of optical coherence tomography (OCT) images for the response of diabetic macular edema (DME) patients to anti-vascular endothelial growth factor (VEGF) therapy generated from baseline images using generative adversarial networks (GANs). METHODS: Patient information, including clinical and imaging data, was obtained from inpatients at the Ophthalmology Department of Qilu Hospital. 715 and 103 pairs of pre-and post-treatment OCT images of DME patients were included in the training and validation sets, respectively. The post-treatment OCT images were used to assess the validity of the generated images. Six different GAN models (CycleGAN, PairGAN, Pix2pixHD, RegGAN, SPADE, UNIT) were applied to predict the efficacy of anti-VEGF treatment by generating OCT images. Independent screening and evaluation experiments were conducted to validate the quality and comparability of images generated by different GAN models. RESULTS: OCT images generated f GAN models exhibited high comparability to the real images, especially for edema absorption. RegGAN exhibited the highest prediction accuracy over the CycleGAN, PairGAN, Pix2pixHD, SPADE, and UNIT models. Further analyses were conducted based on the RegGAN. Most post-therapeutic OCT images (95/103) were difficult to differentiate from the real OCT images by retinal specialists. A mean absolute error of 26.74 ± 21.28 µm was observed for central macular thickness (CMT) between the synthetic and real OCT images. CONCLUSION: Different generative adversarial networks have different prognostic efficacy for DME, and RegGAN yielded the best performance in our study. Different GAN models yielded good accuracy in predicting the OCT-based response to anti-VEGF treatment at one month. Overall, the application of GAN models can assist clinicians in prognosis prediction of patients with DME to design better treatment strategies and follow-up schedules.

Impact Assessment of Big Data on Higher Education Management Based on Time-Varying Clustering Sampling Algorithm.

With the continuous development of social economy, education has received more and more attention. As an important part of higher education, it has been profoundly influenced by the era of data in recent years. In order to evaluate the impact of big data on higher education management, this paper introduced a time-varying lens algorithm (TLA) to analyze the business needs of teachers and education management by sorting out the business processes of education management, education thinking, and education practice. Through learning and mining the corresponding technology, improve the corresponding educational ability, and use big data to assist the management of higher education. The simulation results show that the time-varying clustering sampling algorithm is effective.