Automatic Facial Recognition System Assisted-facial Asymmetry Scale Using Facial Landmarks
- Authors
- Lee, Se A.; Kim, Jin; Lee, Jeon Mi; Hong, Yu-Jin; Kim, Ig-Jae; Lee, Jong Dae
- Issue Date
- Sep-2020
- Publisher
- Lippincott Williams & Wilkins Ltd.
- Keywords
- Autonomic facial nerve grading system; Facial nerve paralysis; Facial asymmetry scale
- Citation
- Otology and Neurotology, v.41, no.8, pp 1140 - 1148
- Pages
- 9
- Journal Title
- Otology and Neurotology
- Volume
- 41
- Number
- 8
- Start Page
- 1140
- End Page
- 1148
- URI
- https://scholarworks.bwise.kr/sch/handle/2021.sw.sch/2489
- DOI
- 10.1097/MAO.0000000000002735
- ISSN
- 1531-7129
1537-4505
- 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.
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- Appears in
Collections - College of Medicine > Department of Otorhinolaryngology > 1. Journal Articles
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