Hierarchical attention transformer provides assistant suggestions for orbital rejuvenation surgery.

BACKGROUND

Early detection of periocular aging is a common concern in cosmetic surgery. Traditional diagnostic and treatment methods often require hospital visits and consultations with plastic surgeons, which are costly and time-consuming. This study aims to develop and evaluate an AI-based decision-making system for periocular cosmetic surgery, utilizing a Hierarchical Attention Transformer (HATrans) model designed for multi-label classification in periocular conditions, allowing for home-based early aging identification.

METHODS

This cross-sectional study was conducted at the Department of Plastic and Reconstructive Surgery at Shanghai Jiao Tong University School of Medicine's Ninth People's Hospital from September 1, 2010, to April 30, 2024. The study enhanced the Vision Transformer (ViT) by adding two specialized branches: the Region Recognition Branch for foreground area identification, and the Patch Recognition Branch for refined feature representation via contrastive learning. These enhancements allowed for better handling of complex periocular images.

RESULTS

The HATrans model significantly outperformed baseline architectures such as ResNet and Swin Transformer, achieving superior accuracy, sensitivity, and specificity in identifying periocular aging. Ablation studies demonstrated the critical role of the hierarchical attention mechanism in distinguishing subtle foreground-background differences, improving the model's performance in smartphone-based image analysis.

CONCLUSION

The HATrans model represents a significant advancement in multi-label classification for facial aesthetics, offering a practical solution for early periocular aging detection at home. The model's robust performance supports its potential for assisting clinical decision-making in cosmetic surgery, facilitating accessible and timely treatment recommendations.