Artificial Intelligence-Driven Video Analysis for Novel Outcome Measures After Smile Reanimation Surgery

Background: Since facial paralysis is a dynamic condition, the analysis of still photographs is not sufficient for measurement of facial reanimation outcomes. This study aimed at evaluating an artificial intelligence (AI)-driven software as a novel video assessment tool for smile reanimation surgery and at comparing it with the Terzis score. Methods: Patients with facial paralysis undergoing smile reanimation surgery between January 2008 and April 2020 were eligible for this retrospective study. Inclusion criteria were at least 6 months of follow-up and availability of both pre- and post-operative video documentation. The software output was given as intensity score (IS) values between 0 and 1, representing emotions/action units (AUs) that are absent or fully present, respectively. Results: During the study period, 240 patients underwent facial reanimation surgery, of whom 63 patients met the inclusion criteria. Postoperatively, the median IS of the happiness emotion and lip corner puller AU increased significantly (p < 0.001). There was a positive correlation of Terzis score with the IS of happiness emotion (r = 0.8) and lip corner puller AU (r = 0.74). Conclusions: The novel AI-driven video analysis is strongly correlated with the Terzis score and shows promise for objective functional outcome evaluation after smile reanimation surgery.