Turn Your Vision into Reality—AI-Powered Pre-operative Outcome Simulation in Rhinoplasty Surgery

Abstract Background The increasing demand and changing trends in rhinoplasty surgery emphasize the need for effective doctor–patient communication, for which Artificial Intelligence (AI) could be a valuable tool in managing patient expectations during pre-operative consultations. Objective To develop an AI-based model to simulate realistic postoperative rhinoplasty outcomes. Methods We trained a Generative Adversarial Network (GAN) using 3,030 rhinoplasty patients’ pre- and postoperative images. One-hundred-one study participants were presented with 30 pre-rhinoplasty patient photographs followed by an image set consisting of the real postoperative versus the GAN-generated image and asked to identify the GAN-generated image. Results The study sample (48 males, 53 females, mean age of 31.6 ± 9.0 years) correctly identified the GAN-generated images with an accuracy of 52.5 ± 14.3%. Male study participants were more likely to identify the AI-generated images compared with female study participants (55.4% versus 49.6%; p = 0.042). Conclusion We presented a GAN-based simulator for rhinoplasty outcomes which used pre-operative patient images to predict accurate representations that were not perceived as different from real postoperative outcomes. Level of Evidence III This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .