The realism of the generated images in terms of human perception was evaluated by the visual Turing test (VTT) on our original website. A deep convolutional GAN was trained on the polar maps to synthesize realistic MPI. A total of 1448 polar maps collected from consecutive patients who underwent MPI for known or suspected coronary artery disease from January 2020 to December 2021 were used for the analysis. In this context, we assessed whether expert cardiologists can detect synthesized myocardial perfusion images (MPI) generated by GAN as fake. Currently, generative models represented by generative adversarial networks (GAN) are increasingly utilized in the research field of cardiology, and their potential risks are also being pointed out. As the quality of image generation by deep learning increases, it is becoming difficult to discern its authenticity from the image alone.
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