Artificial Intelligence for Rhinoplasty Design in Asian Patients

Background: Rhinoplasty is one of the most challenging plastic surgeries because it lacks a uniform standard for preoperative design or implementation. For a long time, rhinoplasties were done without an accurate consensus of aesthetic design between surgeons and patients before surgery and consequently brought unsatisfactory appearance for patients. In recent years, three-dimensional (3D) simulation has been used to visualize the preoperative design of rhinoplasty, and good results have been achieved. However, it still relied on individual aesthetics and experience. The preoperative design remained a huge challenge for inexperienced surgeons and could be time-consuming to perform manually. Therefore, we adopted artificial intelligence (AI) in this work to provide a new idea for automated and efficient preoperative nasal contour design. Methods: We collected a dataset of 3D facial images from 209 patients. For each patient, both the original face and the manually designed face using 3D simulation software were included. The 3D images were transformed into point clouds, based on which we used the modified FoldingNet model for deep neural network training (by pytorch 1.12). Results: The trained AI model gained the ability to perform aesthetic design automatically and achieved similar results to manual design. We analysed the 1027 facial features captured by the AI model and concluded two of its possible cognitive modes. One is to resemble the human aesthetic considerations while the other is to fulfil the given task in a special way of the machine. Conclusion: We presented the first AI model for automated preoperative 3D simulation of rhinoplasty in this study. It provided a new idea for the automated, individual and efficient preoperative design, which was expected to bring a new paradigm for rhinoplasty and even the whole field of plastic surgery. Level of Evidence IV: 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.