Artificial Intelligence Framework for Automated Facial Asymmetry Detection Using Key-Point Analysis and Neural Networks.

Abstract

Accurate facial asymmetry assessment is essential in orthodontics, maxillofacial surgery, and plastic surgery. While minor asymmetry is common, severe cases often result from congenital conditions or trauma. Traditional methods struggle to comprehensively quantify asymmetry's extent and direction. This study developed and compared artificial neural networks (ANN) and Siamese neural networks (SNN) to detect facial asymmetry and determine deviation direction (horizontal/vertical). A dataset of 1200 frontal photographs, annotated by three orthodontists, was used. The MediaPipe model facilitated facial landmark detection and midline alignment. Two approaches were employed: (1) extracting features from facial landmarks and using them to train an ANN, and (2) SNN-based comparison of mirrored facial halves. Exploratory data analysis (EDA) was used to quantify facial asymmetry in both vertical and horizontal dimensions. The ANN and SNN performance was evaluated using accuracy, recall, and F1-score. The SNN outperformed ANN, achieving 97% accuracy and strong agreement with expert evaluations (Cohen's Kappa: 0.84 for asymmetry detection, 0.73 for horizontal deviation, and 0.80 for vertical asymmetry). The symmetry group showed 96.14% mean similarity, while the asymmetry group had 83.97%. The SNN's ROC curve yielded an AUC of 0.98, indicating high diagnostic performance. This study demonstrates the potential of AI-driven methods, particularly SNN, for reliable and objective facial asymmetry assessment in clinical settings. Future research should focus on expanding datasets and refining midline alignment to improve accuracy, especially in cases with vertical eye asymmetry.