Automatic Facial Recognition System Assisted-facial Asymmetry Scale Using Facial Landmarks.
Otol Neurotol
; 41(8): 1140-1148, 2020 09.
Article
em En
| MEDLINE
| ID: mdl-33169952
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. STUDYDESIGN:
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 OUTCOMEMEASURES:
Correlation with Sunnybrook (SB) and House-Brackmann (HB) grading scales.RESULTS:
Our results had good reliability and correlation with other grading systems (râ=â0.905 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.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Paralisia Facial
/
Reconhecimento Facial
Idioma:
En
Ano de publicação:
2020
Tipo de documento:
Article