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 .
ABSTRACT
Lagophthalmos is the incomplete closure of the eyelids posing the risk of corneal ulceration and blindness. Lagophthalmos is a common symptom of various pathologies. We aimed to program a convolutional neural network to automatize lagophthalmos diagnosis. From June 2019 to May 2021, prospective data acquisition was performed on 30 patients seen at the Department of Plastic, Hand, and Reconstructive Surgery at the University Hospital Regensburg, Germany (IRB reference number: 20-2081-101). In addition, comparative data were gathered from 10 healthy patients as the control group. The training set comprised 826 images, while the validation and testing sets consisted of 91 patient images each. Validation accuracy was 97.8% over the span of 64 epochs. The model was trained for 17.3 min. For training and validation, an average loss of 0.304 and 0.358 and a final loss of 0.276 and 0.157 were noted. The testing accuracy was observed to be 93.41% with a loss of 0.221. This study proposes a novel application for rapid and reliable lagophthalmos diagnosis. Our CNN-based approach combines effective anti-overfitting strategies, short training times, and high accuracy levels. Ultimately, this tool carries high translational potential to facilitate the physician's workflow and improve overall lagophthalmos patient care.