From the Division of Plastic and Reconstructive Surgery, Zucker School of Medicine at Hofstra/Northwell; Microsoft Corp.; and the Division of Plastic and Reconstructive Surgery, British Hospital of Buenos Aires.
Plast Reconstr Surg. 2020 Jan;145(1):203-209. doi: 10.1097/PRS.0000000000006342.
Male-to-female transgender patients desire to be identified, and treated, as female, in public and social settings. Facial feminization surgery entails a combination of highly visible changes in facial features. To study the effectiveness of facial feminization surgery, we investigated preoperative/postoperative gender-typing using facial recognition neural networks.
In this study, standardized frontal and lateral view preoperative and postoperative images of 20 male-to-female patients who completed hard- and soft-tissue facial feminization surgery procedures were used, along with control images of unoperated cisgender men and women (n = 120 images). Four public neural networks trained to identify gender based on facial features analyzed the images. Correct gender-typing, improvement in gender-typing (preoperatively to postoperatively), and confidence in femininity were analyzed.
Cisgender male and female control frontal images were correctly identified 100 percent and 98 percent of the time, respectively. Preoperative facial feminization surgery images were misgendered 47 percent of the time (recognized as male) and only correctly identified as female 53 percent of the time. Postoperative facial feminization surgery images were gendered correctly 98 percent of the time; this was an improvement of 45 percent. Confidence in femininity also improved from a mean score of 0.27 before facial feminization surgery to 0.87 after facial feminization surgery.
In the first study of its kind, facial recognition neural networks showed improved gender-typing of transgender women from preoperative facial feminization surgery to postoperative facial feminization surgery. This demonstrated the effectiveness of facial feminization surgery by artificial intelligence methods.
CLINICAL QUESTION/LEVEL OF EVIDENCE: Therapeutic, IV.
男变女跨性别患者希望在公共和社交场合被识别和视为女性。面部女性化手术需要对面部特征进行一系列明显的改变。为了研究面部女性化手术的效果，我们使用面部识别神经网络研究了术前/术后的性别定型。
本研究使用了 20 名接受过硬组织和软组织面部女性化手术的男变女跨性别患者的标准化正面和侧面术前和术后图像，以及未接受手术的顺性别男性和女性的对照图像（n=120 张图像）。四个经过训练以根据面部特征识别性别的公共神经网络分析了这些图像。分析了正确的性别定型、性别定型的改善（术前到术后）以及女性化的信心。
顺性别男性和女性的对照正面图像的正确识别率分别为 100%和 98%。术前面部女性化手术图像的性别错误率为 47%（被识别为男性），只有 53%的时间被正确识别为女性。术后面部女性化手术图像的性别识别率为 98%；这是 45%的改善。女性化的信心也从术前女性化手术前的平均 0.27 分提高到术后的 0.87 分。
在同类研究中，面部识别神经网络显示跨性别女性的术前面部女性化手术到术后面部女性化手术的性别定型得到了改善。这通过人工智能方法证明了面部女性化手术的有效性。
临床问题/证据水平：治疗性，IV。
