# 3D Soft-Tissue Prediction Methodologies for Orthognathic Surgery--A Literature Review
## 1 Introduction  
Patients presenting dentofacial deformities are commonly subject to combined orthodontic and surgical treatment such as Le Fort I osteotomy (LFI), bilateral sagittal split osteotomy (BSSO), intraoral ramus vertical osteotomy (IVRO), sagittal split ramus osteotomy (SSRO), bimaxillary surgery and genioplasty. These interventions have been commonly planned with two-dimensional methodologies; today, the last challenge is the three-dimensional (3D) surgery planning. The refining of 3D graphics and imaging tools gives the chance to explore surgical planning and prediction of the effects of different clinical approaches; these techniques are based on images acquired with computed tomography (CT), cone bean computed tomography (CBCT) and multi-slice computed tomography (MST), which provide volumetric images of facial anatomical structure. Additionally, the surface of the face can be scanned and mapped to underline the effect of changes in facial appearance using 3D laser technology [1] that gives a great contribution to surgeons to decide on the type of surgeries as well as on the magnitude and direction of surgical movements to correct facial dysmorphology. Moreover, a deep interest in the prediction of soft-tissue response to hard tissue movements has been growing and two-dimensional conventional methodologies seem to be insufficient for this aim as they do not take into account the third dimension. Indeed, the possibility to know the soft-tissue response to surgical operations helps surgeons to plan surgical movements and it gives surgeons more information about the need of orthodontic decompensation. Furthermore, the purpose of these interventions is not only to correct the facial dysmorphology from a functional point of view, but also to obtain an aesthetic enhancement of patients' facial aspect. Therefore, an accurate treatment planning is very important to obtain a good aesthetic and occlusal result [2]. For the aforementioned reasons, having a preview of the soft-tissue arrangement is extremely important. In future, this opportunity could bring to have a surgical planning methodology able to best match patients' aesthetic expectations, additionally to the functional corrective aspect. Several methods have been considered to forecast soft tissue responses; the most common are the mass-spring model (MSM), finite element model (FEM) and mass tensor model (MTM). Most of the software packages currently adopted in clinical practice are based on these models. Generally, those softwares seem to reach an acceptable overall accuracy, but with inaccuracies for specific areas of the face, for example around the lips. Studies have been conducted to develop 2D prevision models based on the ratio between facial and bony movements, particularly focusing on face sub regions [3]; by comparing predictive models constructed on this ratio and patients' post-operative conditions, it is stated that traditional approaches show limits in forecasting the outcome of large and complex movements. Moreover, a large number of variables must be selected [4]. Although there are several studies on the soft tissue changes after maxillary osteotomies, few of them report a systematic analysis. The target of this systematic review is to gather information on the existing prediction methods and software of soft tissue displacements after dysmorphism corrective surgery, and to draw conclusions on the accuracy and reliability of these software in the preview of surgical outcome. Additionally, some problems related to the level of accuracy needed by surgeons, to the predictive imprecision reported in the studies and to the magnitude of the acceptable error are presented. This work is structured as follows. The section Material and Methods describes the methods that have been adopted for the literature review. The section Results includes the clinical details of patients involved and the details of the articles considered; the articles have been divided in subsections according to the software used by the authors.