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面部拉皮手术:人工智能和患者满意度量化手术类型和特定技术的价值。

Facelift Surgery Turns Back the Clock: Artificial Intelligence and Patient Satisfaction Quantitate Value of Procedure Type and Specific Techniques.

机构信息

Miami University Medical School, Oxford, OH, USA.

Division of Plastic Surgery, Zucker School of Medicine at Hofstra/Northwell, Hofstra University, Hempstead, NY, USA.

出版信息

Aesthet Surg J. 2021 Aug 13;41(9):987-999. doi: 10.1093/asj/sjaa238.

Abstract

BACKGROUND

Patients desire facelifting procedures to look younger, refreshed, and attractive. Unfortunately, there are few objective studies assessing the success of types of facelift procedures and ancillary techniques.

OBJECTIVES

The authors sought to utilize convolutional neural network algorithms alongside patient-reported FACE-Q outcomes to evaluate perceived age reduction and patient satisfaction following various facelift techniques.

METHODS

Standardized preoperative and postoperative (1-year) images of patients who underwent facelift procedures were analyzed by 4 neural networks to estimate age reduction after surgery (n = 105). FACE-Q surveys were employed to measure patient-reported facial aesthetic outcome. We compared (1) facelift procedure type: skin-only vs superficial musculoaponeurotic system (SMAS)-plication, vs SMAS-ectomy; and (2) ancillary techniques: fat grafting (malar) vs no fat grafting. Outcomes were based on complications, estimated age-reduction, and patient satisfaction.

RESULTS

The neural network preoperative age accuracy score demonstrated that all neural networks were accurate in identifying our patients' ages (mean score = 100.4). SMAS-ectomy and SMAS-plication had significantly greater age-reduction (5.85 and 5.35 years, respectively) compared with skin-only (2.95 years, P < 0.05). Fat grafting compared to no fat grafting demonstrated 2.1 more years of age reduction. Facelift procedure type did not affect FACE-Q scores; however, patients who underwent fat grafting had a higher satisfaction with outcome (78.1 ± 8 vs 69 ± 6, P < 0.05) and decision to have the procedure (83.0 ± 6 vs 72 ± 9, P < 0.05).

CONCLUSIONS

Artificial intelligence algorithms can reliably estimate the reduction in apparent age after facelift surgery. Facelift technique, like SMAS-ectomy or SMAS-plication, and specific technique, like fat grafting, were found to enhance facelifting outcomes and patient satisfaction.

摘要

背景

患者希望通过面部提升手术来显得更年轻、更有精神、更有吸引力。不幸的是,很少有客观研究评估各种面部提升手术类型和辅助技术的成功。

目的

作者试图利用卷积神经网络算法和患者报告的 FACE-Q 结果来评估各种面部提升技术后感知年龄的减少和患者满意度。

方法

对接受面部提升手术的患者的标准化术前和术后(1 年)图像进行了 4 个神经网络的分析,以估计手术后的年龄减少(n=105)。采用 FACE-Q 调查来衡量患者报告的面部美学结果。我们比较了(1)面部提升手术类型:仅皮肤 vs 浅层肌肉筋膜系统(SMAS)-缝合术 vs SMAS-切除术;和(2)辅助技术:脂肪移植(颊部) vs 无脂肪移植。结果基于并发症、估计的年龄减少和患者满意度。

结果

神经网络术前年龄准确性评分表明,所有神经网络都能准确识别我们患者的年龄(平均评分=100.4)。SMAS-切除术和 SMAS-缝合术与仅皮肤相比,年龄减少分别显著更大(5.85 年和 5.35 年,分别),5.35 年(P<0.05)。与无脂肪移植相比,脂肪移植显示出 2.1 年的年龄减少。面部提升手术类型不影响 FACE-Q 评分;然而,接受脂肪移植的患者对结果的满意度更高(78.1±8 分与 69±6 分,P<0.05)和手术决策(83.0±6 分与 72±9 分,P<0.05)。

结论

人工智能算法可以可靠地估计面部提升手术后的年龄减少。面部提升技术,如 SMAS-切除术或 SMAS-缝合术,以及特定技术,如脂肪移植,被发现可以增强面部提升效果和患者满意度。

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