Using Facial Recognition Software to Quantify Perceived Age Reduction in Patients Undergoing Blepharoplasty

Introduction Changes to periorbital morphology, including decreased skin elasticity and ptosis, contribute to the appearance of an aging face. Consequently, many patients seek blepharoplasty surgery to address these changes. However, objective measures of surgical success remain sparse. Therefore, we investigated the ability of convolutional neural networks (CNNs) to assess differences in perceived age before and after blepharoplasty and examined correlations between CNN-generated results and human evaluations. Methods Pre- and postoperative patient blepharoplasty images from inception through December 2023 were extracted from the American Society of Plastic Surgeons website. Patient age, follow-up time, gender, and type of procedure were recorded. Two CNN-based platforms, FacePlusPlus (Beijing, China) and Amazon Rekognition (Seattle, WA), were used to estimate patients' pre- and postoperative ages. Two trained volunteers rated patients' aesthetic changes using the Global Aesthetic Improvement Scale (GAIS). Statistical analyses to compare patients' pre- and postoperative CNN-estimated ages and factors associated with perceived age reduction included paired t tests, linear regressions, and ANOVA tests. Results Ninety-four patients were included in the analysis (mean age, 52.4 ± 10.5 years; 84.0% female). Preoperatively, the CNNs estimated patients to be 2.4 years younger than their true ages (estimated age, 50.0 years; true age, 52.4 years; P < 0.05). Postoperatively, the CNNs perceived an average of 3.2 years of age reduction (estimated preoperative age, 50.0 years; estimated postoperative age, 46.8 years; P < 0.01). Perceived age reduction was not associated with gender, true preoperative age, or procedure type ( P > 0.05). GAIS scores positively correlated with perceived age reduction ( r = 0.33, P < 0.05). Patients estimated as older than their true preoperative age had greater CNN-perceived age reductions compared to those estimated as younger (5.0-year reduction vs. 2.3-year reduction, P < 0.05). The discrepancy between preoperative estimated age and true age correlated with postoperative age reduction ( r = 0.31, P < 0.05). Conclusions Convolutional neural networks quantified reductions in perceived age following blepharoplasty, with results aligning with human evaluations. CNN-perceived age reduction was greatest in patients who appeared older than their true age, particularly for those with larger discrepancies. These findings support the potential utility of CNNs as objective tools for assessing aesthetic outcomes and may help preoperatively guide patient expectations for postoperative age reduction.