Chartier Christian, Watt Ayden, Lin Owen, Chandawarkar Akash, Lee James, Hall-Findlay Elizabeth
McGill University Faculty of Medicine, Montreal, QC, Canada.
Department of Experimental Surgery, McGill University Faculty of Medicine, Montreal, QC, Canada.
Aesthet Surg J Open Forum. 2021 Dec 11;4:ojab052. doi: 10.1093/asjof/ojab052. eCollection 2022.
Managing patient expectations is important to ensuring patient satisfaction in aesthetic medicine. To this end, computer technology developed to photograph, digitize, and manipulate three-dimensional (3D) objects has been applied to the female breast. However, the systems remain complex, physically cumbersome, and extremely expensive.
The authors of the current study wish to introduce the plastic surgery community to BreastGAN, a portable, artificial intelligence (AI)-equipped tool trained on real clinical images to simulate breast augmentation outcomes.
Charts of all patients who underwent bilateral breast augmentation performed by the senior author were retrieved and analyzed. Frontal before and after images were collected from each patient's chart, cropped in a standardized fashion, and used to train a neural network designed to manipulate before images to simulate a surgical result. AI-generated frontal after images were then compared with the real surgical results.
Standardizing the evaluation of surgical results is a timeless challenge which persists in the context of AI-synthesized after images. In this study, AI-generated images were comparable to real surgical results.
This study features a portable, cost-effective neural network trained on real clinical images and designed to simulate surgical results following bilateral breast augmentation. Tools trained on a larger dataset of standardized surgical image pairs will be the subject of future studies.
管理患者期望对于确保美容医学中的患者满意度很重要。为此，已开发出用于拍摄、数字化和处理三维（3D）物体的计算机技术并应用于女性乳房。然而，这些系统仍然复杂、体积庞大且极其昂贵。
本研究的作者希望向整形外科学界介绍BreastGAN，这是一种便携式、配备人工智能（AI）的工具，它基于真实临床图像进行训练，以模拟隆乳效果。
检索并分析了由资深作者进行双侧隆乳手术的所有患者的病历。从每位患者的病历中收集术前和术后的正面图像，以标准化方式裁剪，并用于训练一个神经网络，该网络旨在处理术前图像以模拟手术结果。然后将人工智能生成的术后正面图像与实际手术结果进行比较。
标准化手术结果评估是一项长期存在的挑战，在人工智能合成的术后图像背景下依然存在。在本研究中，人工智能生成的图像与实际手术结果相当。
本研究展示了一种便携式、经济高效的神经网络，它基于真实临床图像进行训练，旨在模拟双侧隆乳后的手术结果。基于更大的标准化手术图像对数据集进行训练的工具将是未来研究的主题。
