Synthetic data has the potential to augment the amount of existing data for training of artificial intelligence models. This can accelerate the transition of technology from concept to clinical use as a tool to detect glaucoma earlier via structural changes. An earlier approach to glaucoma will also drastically reduce medical and surgical interventions while simultane- ously relieving financial burden due to ophthalmology-related costs.
Our solution to this problem utilizes deep learning techniques to generate large volumes of high-quality, labeled synthetic ophthalmic images. We aim for future researchers to utilize synthetic data to accelerate the transition of AI to the clinic setting.