Significant progress has been made in artificial intelligence and computer vision research in recent years. Machine learning methods excel in a wide variety of tasks where sufficient data are available. We describe the application of a deep convolutional neural network for the prediction of treatment indication with anti-vascular endothelial growth factor (VEGF) medications based on central retinal optical coherence tomography (OCT) scans. The neural network classifier was trained with OCT images acquired during routine treatment at the University of Regensburg over the years 2008-2016. In over 95% of the cases the treatment indication was accurately predicted based on a singular OCT B scan without human intervention. Despite promising classification the results of deep learning techniques, should always be controlled by the treating physician because false classification can never be excluded due to the probabilistic nature of the method.     «

Significant progress has been made in artificial intelligence and computer vision research in recent years. Machine learning methods excel in a wide variety of tasks where sufficient data are available. We describe the application of a deep convolutional neural network for the prediction of treatment indication with anti-vascular endothelial growth factor (VEGF) medications based on central retinal optical coherence tomography (OCT) scans. The neural network classifier was trained with OCT image...     »