Park Yun Yong, Kim Kenneth K, Park Bumjin
From the Department of Plastic and Reconstructive Surgery, iWELL Plastic Surgery Clinic, Seoul, South Korea.
Division of Plastic and Reconstructive Surgery, David Geffen School of Medicine at University of California, Los Angeles, Los Angeles, Calif.
Plast Reconstr Surg Glob Open. 2024 Aug 13;12(8):e6055. doi: 10.1097/GOX.0000000000006055. eCollection 2024 Aug.
Overdevelopment of zygomatic bones often results in protrusion and flaring of the midfacial region. This makes the face appear squarer than the more favorable oval shape. Therefore, zygoma reduction surgery has become a commonly performed procedure in patients seeking to obtain an ideal facial shape. Facial soft-tissue ptosis is one of the main complications of zygoma reduction surgery. Previously, the evaluation of cheek soft-tissue ptosis was subjectively based on patients and surgeons. Our study aimed to provide an objective evaluation of soft-tissue sagging in the cheek region after zygoma reduction surgery using artificial intelligence (AI).
We used AI to evaluate cheek sagging in a series of patients who underwent zygoma reduction surgery. We used four methods: tracking facial landmarks, detecting changes in the cheek curvature, and examining changes in the nasolabial fold and marionette lines. Then, the obtained numerical results were assessed for statistically significant differences using statistical validation methods.
Use of AI with the four methods demonstrated no statistically significant differences between the pre- and postsurgery evaluations. AI analysis demonstrated that soft-tissue ptosis did not occur in our series of patients.
AI offers objective evaluation for both patients and doctors. Future research could build on this application to examine various influencing factors and develop new tools using machine learning to evaluate and predict the extent of cheek sagging in patients before surgery.
颧骨过度发育常导致面部中部区域突出和扩宽。这使得面部看起来比更理想的椭圆形更方。因此，颧骨缩小手术已成为寻求理想面部形状的患者中常用的手术。面部软组织下垂是颧骨缩小手术的主要并发症之一。以前，对脸颊软组织下垂的评估基于患者和外科医生的主观判断。我们的研究旨在使用人工智能（AI）对颧骨缩小手术后脸颊区域的软组织下垂进行客观评估。
我们使用AI对一系列接受颧骨缩小手术的患者的脸颊下垂情况进行评估。我们采用了四种方法：跟踪面部标志点、检测脸颊曲率变化、检查鼻唇沟和木偶纹的变化。然后，使用统计验证方法对获得的数值结果进行统计学显著差异评估。
使用AI的四种方法在术前和术后评估之间未显示出统计学显著差异。AI分析表明，我们的一系列患者中未发生软组织下垂。
AI为患者和医生提供了客观评估。未来的研究可以在此应用基础上，研究各种影响因素，并使用机器学习开发新工具，以在手术前评估和预测患者脸颊下垂的程度。
