Deep learning-based automatic facial symmetry scoring in peripheral facial palsy

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.