인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
Unilateral peripheral facial palsy (PFP) results in facial asymmetry and functional impairment, reducing quality of life. Accurate, objective assessment is vital for monitoring and rehabilitation. This study presents an automated method utilizes standardized 2D photographs to visualize facial dynamics using heatmaps and calculates an objective symmetry score, quantifying movement symmetry. Retrospective analysis included 405 facial datasets from 198 PFP patients. Images were processed using a deep learning-based facial landmark detection and an affine alignment algorithm. Heatmaps were generated from grayscale difference images, and symmetry scores calculated by comparing mirrored facial halves within a defined mask. Stennert movement scores were correlated with symmetry scores using Spearman's rank correlation. The method was applied successfully to all datasets, with symmetry scores ranging from 0 to 0.99 (mean 0.85 ± 0.12), varying by expression level. Heatmaps highlighted asymmetries matching clinical findings. In 85% of cases, Stennert trends aligned with symmetry scores; 9% showed stable Stennert scores but changing symmetry scores, suggesting higher sensitivity. Significant negative correlations (r = - 0.32 to - 0.66, p < 0.0001) confirmed greater clinical severity corresponds to lower symmetry scores. In conclusion, the automated method provides an objective, reliable, and accessible tool for assessing facial symmetry in PFP, thereby improving clinical evaluation and facilitating precise rehabilitation monitoring.
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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