Lee, Se A. Kim, Jin Lee, Jeon Mi Hong, Yu-Jin Kim, Ig-Jae Lee, Jong Dae 2024-01-19T16:34:32Z 2024-01-19T16:34:32Z 2021-09-02 2020-09 1531-7129 https://pubs.kist.re.kr/handle/201004/118182 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. English LIPPINCOTT WILLIAMS & WILKINS RELIABILITY SUNNYBROOK Automatic Facial Recognition System Assisted-facial Asymmetry Scale Using Facial Landmarks Article 10.1097/MAO.0000000000002735 1 OTOLOGY & NEUROTOLOGY, v.41, no.8, pp.1140 - 1148 OTOLOGY & NEUROTOLOGY 41 8 1140 1148 scie scopus 000588497000037 2-s2.0-85096080200 Clinical Neurology Otorhinolaryngology Neurosciences & Neurology Otorhinolaryngology Article RELIABILITY SUNNYBROOK Autonomic facial nerve grading system Facial nerve paralysis Facial asymmetry scale