안면마비 평가 도구에 관한 개발 현황 리뷰: 자동화 방식을 중심으로

A Review on Development Trends of Facial Palsy Grading System: Mainly on Automatic Method

Objectives : Grading facial paralysis plays a crucial role in establishing treatment plans and determining prognosis. Both domestic and international efforts have actively focused on the development of objective automated grading systems. The purpose of this study is to investigate trends in Automated Facial Palsy Grading Systems through literature search. Methods : Using PubMed, OASIS, and other databases, related research papers published from 2000 to 2024 were analyzed. The studies were analyzed by dividing into Automated Grading Image Processing (2D, 3D, Video), Landmark Assignment and Standard Grading System for comparison. Results : A total of 13 studies were selected in this study. The first article was published in 2013. Studies were rapidly increased since 2019. The mostly used input data was photo. The other data were 3D photo and video. Studies using 68 landmark assignment system were the most prevalent, followed by OpenCV, Manual attachment, and other Machine Learning algorithms. There were 6 studies compared with House-Brackmann grading system, 4 studies compared with Sunny brook facial grading system, 3 studies compared with Nottingham scale and also studies involving Yanagihara grading system, eFACE, electroneurography (ENoG). Conclusions : The automated Grading systems were developed using various technologies, including image processing, automatic landmark assignment, OpenCV, machine learning, and applications. This automated grading systems demonstrated accuracy comparable to or exceeding that of conventional subjective evaluation methods. Moreover, it significantly reduced the time required for facial paralysis assessment, highlighting its potential as a viable alternative to subjective evaluation.