AI Prediction for Post-Lower Blepharoplasty Age Reduction.

Abstract

Background: Aesthetic standards vary and are subjective; artificial intelligence (AI), which is currently seeing a boom in interest, has the potential to provide objective assessment.

Objectives: The aim of this study was to provide a relatively objective assessment of the aesthetic outcomes of lower blepharoplasty-related surgeries, thereby enhancing the decision-making process and understanding of the surgical results.

Methods: This study included 150 patients who had undergone lower blepharoplasty-related surgeries. Analysis was performed with FaceAge software, created by the authors' research team, which included 4 publicly available age estimation convolution neural network (CNN) models: Amazon Rekognition (Seattle, WA), Microsoft Azure Face (Redmond, WA), Face++ Detect (Beijing, China), and Inferdo face detection (New York, NY). This application was used to compare the subjects' real age and their age as estimated by the 4 CNNs. In addition, this application was used to estimate patient age based on preoperative and postoperative images of all 150 patients and to evaluate the effect of lower blepharoplasty.

Results: In terms of accuracy in age prediction, all CNN models exhibited a certain degree of accuracy. For all 150 patients undergoing lower blepharoplasty-related surgeries, these surgeries resulted in about 2 years of rejuvenation with a statistically significant difference; for the sex difference, men had more age reduction than women also with a statistically significant difference; quadrilateral blepharoplasty showed the most significant antiaging effect.

Conclusions: By using deep-learning models, lower blepharoplasty-related surgeries actually had an effect on perceived age reduction. Deep learning models have the potential to provide quantitative evidence for the rejuvenating effects of blepharoplasty and other cosmetic surgeries.