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

Abstract

Purpose It is clinically important to accurately predict facial soft tissue changes prior to orthognathic surgery. However, the current simulation methods are problematic, especially in anatomic regions of clinical significance, e.g., the nose, lips and chin. We developed a new 3-stage finite element method (FEM) approach that incorporates realistic tissue sliding to improve such prediction. Methods In Stage One, soft-tissue-change was simulated using FEM with patient-specific mesh models generated from our previously developed eFace template. Postoperative bone movement was applied on the patient mesh model with standard FEM boundary conditions. In Stage Two, the simulation was improved by implementing sliding effects between gum tissue and teeth using a nodal force constraint scheme. In Stage Three, the result of the tissue sliding effect was further enhanced by reassigning the soft tissue-bone mapping and boundary conditions using nodal spatial constraint. Finally, our methods have been quantitatively and qualitatively validated using 40 retrospectively evaluated patient cases by comparing it to the traditional FEM method and the FEM with sliding effect using a nodal force constraint method. Results The results showed that our method was better than the other two methods. Using our method, the quantitative distance errors between predicted and actual patient surfaces for the entire face and any sub-regions thereof were below 1.5 mm. The overall soft tissue change prediction was accurate to within 1.1 ± 0.3 mm, with the accuracy around the upper and lower lip regions of 1.2 ± 0.7 mm and 1.5 ± 0.7 mm, respectively. The results of qualitative evaluation completed by clinical experts showed an improvement of 46% in acceptance rate compared to the traditional FEM simulation. More than 80% of the result of our approach was considered acceptable in comparison with 55% and 50% following the other two methods. Conclusion The FEM simulation method with improved sliding effect showed significant accuracy improvement in the whole face and the clinically significant regions (i.e., nose and lips) in comparison with the other published FEM methods, with or without sliding effect using a nodal force constraint. The qualitative validation also proved the clinical feasibility of the developed approach.

3D Soft-Tissue Prediction Methodologies for Orthognathic Surgery—A Literature Review

“…Several approaches have been considered to make a mathematical three-dimensional prediction of soft tissue changes after orthognathic surgery. MSM, FEM [5,6] and MTM are the most common. These have been developed into software packages which are currently used in clinical practice.…”

Three-dimensional region-based study on the relationship between soft and hard tissue changes after orthognathic surgery in patients with prognathism

“…Recent advances in virtual surgical planning combined with commercial software opened up new possibilities in predicting facial soft tissue changes following orthognathic surgery based on the mass sprung model, finite element model, and mass tensor mode algorithms. However, the prediction accuracy in critical regions such as the lips, and chin is required to improve [ 32 , 33 ].…”

Accuracy of three-dimensional virtual simulation of the soft tissues of the face in OrtogOnBlender for correction of class II dentofacial deformities: an uncontrolled experimental case-series study

“…It is based on Blender ( Blender Foundation ), a free, open-source software suite for 3D modeling, animation , texture mapping , compositing , rendering , and video editing [ 33 , 38 ]. Computational deformation methods for simulation of soft tissue surgical outcomes on a face mesh described in the literature include the mass spring model [ 28 , 30 ], Tetrahedral Mass Tensor Model (non-rigid TPS-RPM) [ 32 ], Tetrahedral Mass Spring Model [ 31 ], Mass Tensor Model [ 40 ], finite element model (FEM) [ 27 ], and FEM with slide effects [ 17 , 34 ]. OOB uses the well-established mass spring system mathematical deformation method, which employs a surface-based calculation system as its soft-body deformation model [ 33 ].…”

“…Some algorithms have been developed and incorporated into commercially available predictive software packages to quantify changes in facial soft tissues in three dimensions [ 3 , 17 , 18 , 27 – 30 ].…”

“…Existing commercial programs use soft tissue deformation methods to simulate the outcome on a face mesh, such as the mass spring model [ 28 , 30 ], TPS-RPM [ 31 ], Mass Tensor Model [ 32 ], finite element model (FEM) [ 27 ], and FEM with realistic tissue sliding effects [ 17 ]. On the other hand, free and open-source software has also been incorporated into studies of hard and soft tissue movement [ 4 , 33 , 34 ].…”