In Facial plastic surgery & aesthetic medicine
Background: The nasolabial fold (NLF) greatly contributes to facial aesthetics; changes to NLF depth and vector are disfiguring in patients with facial paralysis (FP). NLF parameters are integral to clinician-graded outcomes, but automated programs currently lack NLF identification capabilities. Objective: To incorporate an automated NLF identification and quantification function into the facial landmark program, Emotrics, and to compare new Emotrics-derived NLF data to clinician-graded electronic facial paralysis assessment (eFACE) data for accuracy. Methods: Photographs of 135 patients with FP were marked bilaterally, using identification markers manually placed on each NLF. A machine learning model was trained to automatically localize the markers using these data. Once Emotrics accurately identified the NLF and its corresponding vector, photographs of 20 additional patients who underwent facial reanimation procedures were assessed by the algorithm. Results: The enhanced Emotrics algorithm successfully identified the NLF, and measured the vector from midline, in a series of patients with FP. NLF vector data closely matched corresponding eFACE parameters. Furthermore, changes in NLF presence and vector were detected following facial reanimation procedures. Conclusion: The Emotrics program now provides critical NLF data, providing objective parameters for clinicians interested in changing NLF dynamics after FP.
Ein Liliana, Trzcinski Lauren, Perry Luke, Bark Kee Yoon, Hadlock Tessa, Guarin Diego L
2023-Mar-01
