Automatic Facial Recognition System Assisted-facial Asymmetry Scale Using Facial Landmarks.

ABSTRACT

OBJECTIVES: This study aimed to demonstrate the application of our automated facial recognition system to measure facial nerve function and compare its effectiveness with other conventional systems and provide a preliminary evaluation of deep learning-facial grading systems. STUDY DESIGN: Retrospective, observational. SETTING: Tertiary referral center, hospital. PATIENTS Facial photos taken from 128 patients with facial paralysis and two persons with no history of facial palsy were analyzed. INTERVENTION Diagnostic. MAIN OUTCOME MEASURES: Correlation with Sunnybrook (SB) and House-Brackmann (HB) grading scales. RESULTS: Our results had good reliability and correlation with other grading systems (r = 0.905 and 0.783 for Sunnybrook and HB grading scales, respectively), while being less time-consuming than Sunnybrook grading scale. CONCLUSIONS: Our objective method shows good correlation with both Sunnybrook and HB grading systems. Furthermore, this system could be developed into an application for use with a variety of electronic devices, including smartphones and tablets.