From the Division of Plastic and Reconstructive Surgery, Zucker School of Medicine at Hofstra/Northwell; and Microsoft Corp.
Plast Reconstr Surg. 2021 Jul 1;148(1):45-54. doi: 10.1097/PRS.0000000000008020.
Patients desire face-lifting procedures primarily to appear younger, more refreshed, and attractive. Because there are few objective studies assessing the success of face-lift surgery, the authors used artificial intelligence, in the form of convolutional neural network algorithms alongside FACE-Q patient-reported outcomes, to evaluate perceived age reduction and patient satisfaction following face-lift surgery.
Standardized preoperative and postoperative (1 year) images of 50 consecutive patients who underwent face-lift procedures (platysmaplasty, superficial musculoaponeurotic system-ectomy, cheek minimal access cranial suspension malar lift, or fat grafting) were used by four neural networks (trained to identify age based on facial features) to estimate age reduction after surgery. In addition, FACE-Q surveys were used to measure patient-reported facial aesthetic outcome. Patient satisfaction was compared to age reduction.
The neural network preoperative age accuracy score demonstrated that all four neural networks were accurate in identifying ages (mean score, 100.8). Patient self-appraisal age reduction reported a greater age reduction than neural network age reduction after a face lift (-6.7 years versus -4.3 years). FACE-Q scores demonstrated a high level of patient satisfaction for facial appearance (75.1 ± 8.1), quality of life (82.4 ± 8.3), and satisfaction with outcome (79.0 ± 6.3). Finally, there was a positive correlation between neural network age reduction and patient satisfaction.
Artificial intelligence algorithms can reliably estimate the reduction in apparent age after face-lift surgery; this estimated age reduction correlates with patient satisfaction.
CLINICAL QUESTION/LEVEL OF EVIDENCE: Diagnostic, IV.
患者对面部提升手术的主要诉求是看起来更年轻、更有精神、更有吸引力。由于很少有客观研究评估面部提升手术的成功，作者使用人工智能，以卷积神经网络算法和 FACE-Q 患者报告结果的形式，评估面部提升手术后的感知年龄减少和患者满意度。
连续 50 例接受面部提升手术（颈阔肌成形术、浅表肌肉腱膜系统切除术、面颊微创颅悬提颧骨提升术或脂肪移植术）的患者的标准化术前和术后（1 年）图像被 4 个神经网络（基于面部特征识别年龄）用于估计手术后的年龄减少。此外，FACE-Q 调查用于衡量患者报告的面部美学结果。将患者满意度与年龄减少进行比较。
神经网络术前年龄准确性评分表明，所有四个神经网络都能准确识别年龄（平均评分 100.8）。与神经网络估计的年龄减少相比，患者自我评估的面部提升后年龄减少更大（-6.7 岁比-4.3 岁）。FACE-Q 评分显示患者对面部外观（75.1±8.1）、生活质量（82.4±8.3）和对结果的满意度（79.0±6.3）的满意度很高。最后，神经网络估计的年龄减少与患者满意度之间存在正相关。
人工智能算法可以可靠地估计面部提升手术后的表观年龄减少，这种估计的年龄减少与患者满意度相关。
临床问题/证据水平：诊断，IV。
