Knoedler Samuel, Alfertshofer Michael, Simon Siddharth, Panayi Adriana C, Saadoun Rakan, Palackic Alen, Falkner Florian, Hundeshagen Gabriel, Kauke-Navarro Martin, Vollbach Felix H, Bigdeli Amir K, Knoedler Leonard
Division of Plastic Surgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Department of Plastic and Hand Surgery, Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany.
Aesthetic Plast Surg. 2024 Dec;48(23):4833-4838. doi: 10.1007/s00266-024-04043-9. Epub 2024 May 22.
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.
To develop an AI-based model to simulate realistic postoperative rhinoplasty outcomes.
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.
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).
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.
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隆鼻手术需求的不断增加和趋势的变化凸显了有效医患沟通的必要性，而人工智能（AI）可能是术前咨询中管理患者期望的宝贵工具。
开发一种基于人工智能的模型来模拟逼真的隆鼻术后效果。
我们使用3030例隆鼻患者的术前和术后图像训练了一个生成对抗网络（GAN）。101名研究参与者观看了30张隆鼻术前患者照片，随后观看一组由真实术后图像与GAN生成图像组成的图像集，并被要求识别GAN生成的图像。
研究样本（48名男性，53名女性，平均年龄31.6±9.0岁）正确识别GAN生成图像的准确率为52.5±14.3%。与女性研究参与者相比，男性研究参与者更有可能识别出人工智能生成的图像（55.4%对49.6%；p = 0.042）。
我们展示了一种基于GAN的隆鼻效果模拟器，该模拟器使用术前患者图像来预测与真实术后效果无异的准确图像。
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