# 3D Soft-Tissue Prediction Methodologies for Orthognathic Surgery--A Literature Review
## 3 Results
### Prediction Methodologies
#### 3.2.3 Works Based on Other 3D Software
According to clinical options, the software generated a set of simulations and models of the soft tissues; in order to prevent temporomandibular functional problems, an orthodontist assessed the pre-operative plans. From the comparison of simulation and CT surgical outcomes, it was found that the prediction of soft tissue situation had a reliability greater than 91%, with a percentage of error lower than 2 mm for 76%-99%. According to the obtained results, SurgiCase_CMF was able to give a realistic and accurate preview of the face of the subjects undergoing surgery, but there were significant errors in the lip and chin regions, as in the previous studies [30]. Figure 5: Virtual surgical planning proposed by [PERSON] et al. [30]. 3 dMDvultus is a software package in which the rendering function is based on Mass Spring Model. MSM assumes a discretization of a deformable object into n mass points and a set of m connections between each n mass point. In a tetrahedral discretization, for each mesh node a point is allocated, and it is defined a linear spring for all the edges of the mesh. This linear spring follows [PERSON]'s law. This model has the advantage of being simple and computationally efficient, but the disadvantage is that the elastic behavior of the model is determined by the spring constant; the value of this constant is an approximation and it has no true bio-mechanical relevance [19]. [PERSON] et al. used 3 dMDvultus to predict the soft tissue changes after surgery. The work included 10 patients who underwent Le Fort I osteotomy. Ten landmarks were used to measure the distances between the pre- and post- operative meshes. Moreover, the percentage of mesh points minor or equal to 2.0 mm were calculated for the full face and for specific anatomical regions. The results demonstrated that the percentage of mesh with errors minor or equal to 2.0 mm for the full face was 85.2%-94.5% and 31.3%-100% for anatomical regions. The range of root mean square (RMS) error was from 2.49 mm to 0.94 mm. The most of mean linear distances computed between the surfaces was equal or less than 0.8 mm, but it increased for the mean absolute distances. From this analysis, it was stated that the use of specific anatomical regions was more significant in clinical practice than the use of full face [31]. As in the previous article, the software considered by [PERSON] et al. is 3 dMDvultus. Three-dimensional photographs of patients' head in natural position (lips and muscles at rest, open eyes, neutral facial expression) of pre- and post-operative were taken; CT and/or CBCT images were imported to the software platform. Pre-operative 3D photograph was fused with pre-operative CT/CBCT images. In the same manner, post-operative bony skull data was fused with pre-operatives by matching bone areas not involved in surgeries. According to actual post-surgical CT, 3D bone segments were reproduced in the pre-operative skull; the software rendering function (mass spring model) was activated. Osteotomy segments were moved to real post-operative position and the soft-tissue rendering generated the textured facial soft tissue prediction. After the fusion of 3D photographs of predicted and real post-operative soft tissue, a horizontal plane was positioned arbitrarily to divide the face in an upper part not involved in surgery, and a lower part including the overlap of simulated and real post-surgery result. Averaged distribution of absolute error showed more discrepancies between predicted and real post-operative outcome in the lower part of the face: in 29.8% of lower halves authors found errors exceeding 3.00 mm. This preliminary study concluded that the software platform had an insufficient accuracy in the forecast of 3D soft-tissue displacements [32]. [PERSON] et al. carried out a retrospective study in which they focused on the ability of 3 dMDvultus to give a prediction of the face appearance of patients undergoing LFI maxillary advancement.