A generative adversarial network approach to predicting postoperative appearance after orbital decompression surgery for thyroid eye disease

Purpose: Orbital decompression for thyroid-associated ophthalmopathy (TAO) is an ophthalmic plastic surgery technique to prevent optic neuropathy and reduce exophthalmos. Because the postoperative appearance can significantly change, sometimes it is difficult to make decisions regarding decompression surgery. Herein, we present a deep learning technique to synthesize the realistic postoperative appearance for orbital decompression surgery. Methods: This data-driven approach is based on a conditional generative adversarial network (GAN) to transform preoperative facial input images into predicted postoperative images. The conditional GAN model was trained on 109 pairs of matched pre- and postoperative facial images through data augmentation. Results: When the conditional variable was changed, the synthesized facial image was transferred from a preoperative image to a postoperative image. The predicted postoperative images were similar to the ground truth postoperative images. We also found that GAN-based synthesized images can improve the deep learning classification performance between the pre- and postoperative status using a small training dataset. However, a relatively low quality of synthesized images was noted after a readout by clinicians. Conclusions: Using this framework, we synthesized TAO facial images that can be queried using conditioning on the orbital decompression status. The synthesized postoperative images may be helpful for patients in determining the impact of decompression surgery. However, the quality of the generated image should be further improved. The proposed deep learning technique based on a GAN can rapidly synthesize such realistic images of the postoperative appearance, suggesting that a GAN can function as a decision support tool for plastic and cosmetic surgery techniques.