AI-assisted mesh generation for subject-specific modeling of facial soft tissues

Purpose: Simulation of reconstructive and cosmetic facial surgeries, such as orthognathic surgery, requires precise, patient-specific soft tissue meshes for outcome prediction. Conventional meshing methods rely on labor-intensive processes, including manual landmark digitization and mesh editing, and often lack point correspondence among subjects. These limitations reduce their efficiency, scalability, and utility in fast-paced clinical environments, highlighting the need for innovative and streamlined meshing techniques. Methods: This study presents a novel AI-assisted mesh generation (AAMG) approach using Google MediaPipe for real-time facial landmark detection to automate the creation of volumetric meshes of facial soft tissues. By leveraging these landmarks as reference points, the AAMG method generates detailed meshes that accurately reflect individual facial anatomy without manual intervention. To evaluate performance, we compared our automated method with a clinically validated, expert-guided mesh generation (EGMG) method that relies on manual landmark digitization and mesh editing. Both methods were tested on a dataset of 29 subjects who had undergone orthognathic surgery. Results: The AAMG method demonstrated high-quality metrics, with a mean Jacobian ratio of 0.83, skewness of 0.25, and an aspect ratio of 2.15, comparable to the EGMG method. Additionally, Chamfer distance analysis showed no significant differences affecting simulation performance between the two methods. Conclusion: The proposed AI-assisted mesh generation method significantly reduces mesh generation time from several hours to under a minute, while maintaining comparable mesh quality and accuracy to a clinically validated, expert-guided mesh generation method. Our method ensures consistent subject-specific meshing by leveraging real-time landmark detection and automated interpolation, improving workflow efficiency for surgical planning.