Generative Adversarial Networks in Cardiology

Abstract

Generative adversarial networks (GANs) are state-of-the-art neural network models used to synthesise images and other data. GANs brought a considerable improvement to the quality of synthetic data, quickly becoming the standard for data-generation tasks. In this work, we summarise the applications of GANs in the field of cardiology, including generation of realistic cardiac images, electrocardiography signals, and synthetic electronic health records. The utility of GAN-generated data is discussed with respect to research, clinical care, and academia. And we present illustrative examples of our GAN-generated cardiac magnetic resonance and echocardiography images, showing the evolution in image quality across 6 different models, which have become almost indistinguishable from real images. Finally, we discuss future applications, such as modality translation or patient trajectory modelling. Moreover, we discuss the pending challenges that GANs need to overcome, namely, their training dynamics, the medical fidelity or the data regulations and ethics questions, to become integrated in cardiology workflows.