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.