Artificial Intelligence in Surgical Evaluation: A Study of Facial Rejuvenation Techniques

Background: Aesthetic facial surgeries historically rely on subjective analysis in determining success; this limits objective comparison of surgical outcomes. Objectives: This case study exemplifies the use of an artificial intelligence software on objectively analyzing facial rejuvenation techniques with the aim of reducing subjective bias. Methods: Retrospectively, all patients who underwent facial rejuvenation surgery with concomitant procedures from 2015 to 2017 were included (n = 32). Patients were categorized into Groups A to C: Group A-10 superficial musculoaponeurotic system (SMAS) plication facelift (n = 10), Group B-SMASectomy facelift (n = 7), and Group C-high SMAS facelift (n = 15). Neutral repose images preoperatively and postoperatively (average >3 months) were analyzed using artificial intelligence for emotion and action unit alterations. Results: Postoperatively, Group A experienced a decrease in happiness by 0.84% and a decrease in anger by 6.87% (P >> .1). Group B had an increase in happiness by 0.77% and an increase in anger by 1.91% (P >> .1). Both Group A and Group B did not show any discernable action unit patterns. In Group C, the lip corner puller AU increased in average intensity from 0% to 18.7%. This correlated with an average increase in detected happiness from 1.03% to 13.17% (P = .008). Conversely, the average detected anger decreased from 14.66% to 0.63% (P = .032). Conclusions: This study provides the first proof of concept for the use of a machine learning software application to objectively assess various aesthetic surgical outcomes in facial rejuvenation. Due to limitations in patient heterogeneity, this study does not claim one technique's superiority but serves as a conceptual foundation for future investigation.