A clinically validated prediction method for facial soft-tissue changes following double-jaw surgery

Area of Science:

• Biomedical Engineering
• Computational Mechanics
• Plastic Surgery

Background:

• Accurate prediction of facial soft-tissue changes is crucial for successful orthognathic surgery outcomes.
• Current finite element method (FEM) simulations face limitations in predicting changes in key facial regions such as the nose, lips, and chin.

Purpose of the Study:

• To develop and validate a novel 3-stage FEM approach incorporating realistic tissue sliding for improved prediction of facial soft-tissue alterations after orthognathic surgery.
• To address the inaccuracies of traditional FEM methods in clinically significant facial areas.

Main Methods:

• A 3-stage FEM process was employed, starting with patient-specific mesh models and standard boundary conditions.
• Stage Two introduced tissue sliding simulation between gums and teeth using nodal force constraints.
• Stage Three refined the simulation by adjusting soft-tissue-bone mapping and boundary conditions with nodal spatial constraints.

Main Results:

• The proposed FEM method achieved quantitative accuracy within 1.1 ± 0.3 mm for overall soft-tissue change prediction, with errors below 1.5 mm for all facial regions.
• Accuracy in the upper and lower lip regions was 1.2 ± 0.7 mm and 1.5 ± 0.7 mm, respectively.
• Clinical expert evaluation showed a 46% increase in acceptance rate compared to traditional FEM, with over 80% of results deemed acceptable.

Conclusions:

• The enhanced FEM simulation with improved tissue sliding significantly improves prediction accuracy for facial soft-tissue changes, especially in the nose and lips.
• The method demonstrates superior performance compared to existing FEM techniques, with or without sliding effects.
• Qualitative validation confirms the clinical feasibility and effectiveness of the developed approach for orthognathic surgery planning.