Yassa Arsany, Akhavan Arya, Ayad Solina, Ayad Olivia, Colon Anthony, Ignatiuk Ashley
From Division of Plastic and Reconstructive Surgery, Rutgers New Jersey Medical School, Newark, N.J.
Department of Computer Engineering, The British University in Egypt - BUE, El Sherouk City, Egypt.
Plast Reconstr Surg Glob Open. 2024 Dec 20;12(12):e6295. doi: 10.1097/GOX.0000000000006295. eCollection 2024 Dec.
Given the public's tendency to overestimate the capability of artificial intelligence (AI) in surgical outcomes for plastic surgery, this study assesses the accuracy of AI-generated images for breast augmentation and reduction, aiming to determine if AI technology can deliver realistic expectations and can be useful in a surgical context.
We used AI platforms GetIMG, Leonardo, and Perchance to create pre- and postsurgery images of breast augmentation and reduction. Board-certified plastic surgeons and plastic surgery residents evaluated these images using 11 metrics and divided them into 2 categories: realism and clinical value. Statistical analysis was conducted using analysis of variance and Tukey honestly significant difference post hoc tests. Images of the nipple-areolar complex were excluded due to AI's nudity restrictions.
GetIMG (mean ± SD) (realism: 3.83 ± 0.81, clinical value: 3.13 ± 0.62), Leonardo (realism: 3.30 ± 0.69, clinical value: 2.94 ± 0.47), and Perchance (realism: 2.68 ± 0.77, clinical value: 2.88 ± 0.44) showed comparable realism and clinical value scores with no significant difference ( > 0.05). In specific metrics, GetIMG outperformed significantly in surgical relevance compared with the other models ( values: 0.02 and 0.03). Healing and scarring prediction is the metric that underperformed across models (2.25 ± 1.11 ≤ 0.03). Panelists found some images "cartoonish" with unrealistic skin, indicating AI origin.
The AI models showed similar performance, with some images accurately predicting postsurgical outcomes, particularly breast size and volume in a bra. Despite this promise, the absence of detailed nipple-areola complex visualization is a significant limitation. Until these features and consistent representations of various body types and skin tones are achievable, the authors advise using actual patient photographs for consultations.
鉴于公众倾向于高估人工智能(AI)在整形手术外科手术结果方面的能力,本研究评估了AI生成的隆胸和缩胸图像的准确性,旨在确定AI技术是否能够给出符合现实的预期,以及在手术环境中是否有用。
我们使用AI平台GetIMG、Leonardo和Perchance创建隆胸和缩胸手术前后的图像。获得委员会认证的整形外科医生和整形外科住院医师使用11项指标对这些图像进行评估,并将其分为两类:真实感和临床价值。使用方差分析和Tukey真实显著性差异事后检验进行统计分析。由于AI的裸露限制,乳头乳晕复合体的图像被排除。
GetIMG(均值±标准差)(真实感:3.83±0.81,临床价值:3.13±0.62)、Leonardo(真实感:3.30±0.69,临床价值:2.94±0.47)和Perchance(真实感:2.68±0.77,临床价值:2.88±0.44)显示出相当的真实感和临床价值得分,无显著差异(>0.05)。在特定指标中,与其他模型相比,GetIMG在手术相关性方面表现显著更好(值:0.02和0.03)。愈合和瘢痕预测是所有模型中表现不佳的指标(2.25±1.11≤0.03)。小组成员发现一些图像带有不真实皮肤,显得“卡通化”,显示出AI生成的痕迹。
AI模型表现出相似的性能,一些图像能够准确预测术后结果,特别是胸罩内乳房的大小和体积。尽管有此前景,但缺乏乳头乳晕复合体的详细可视化是一个重大限制。在能够实现这些特征以及各种体型和肤色的一致呈现之前,作者建议使用实际患者照片进行咨询。