# 3D Soft-Tissue Prediction Methodologies for Orthognathic Surgery--A Literature Review
## 4 Discussion and Conclusions  
In this systematic review we have summarized twenty-four studies focused on the evaluation of soft-tissue prediction software for maxillofacial surgery. The treated software are commonly used in clinical procedures to have a preview of the patients' outcome after corrective surgery; the most common methodologies on which these software are based are the mass spring model, the finite element model and the mass tensor model, each of them with its advantages and disadvantages. MSM presents an easy architecture and a low memory usage but it has no real biomechanical foundations; FEM is more relevant from a biomechanical point of view, but it has a high computational cost and a rather long simulation time; MTM tries to combine the advantages of MSM and of FEM [18]. In particular, the lip region is the most difficult to predict, since lips do not rely on bony structures; moreover, it is difficult to replicate the same lip position from one acquisition to another. Due to this particularity, lips are supposed to be particularly involved in involuntary displacements caused by surgeries performed on different facial areas. The difficulty in accurately predicting the appearance of this sub region is a crucial point in the development of a prediction method, since it is particularly related to aesthetic self-perception and satisfaction of the patient. With respect to the traditional algorithms based on hard- to soft-tissue ratio, methodologies involving biomechanical models of viscoelasticity of the soft tissue to mimic its elastic deformation seems to be able to give a more realistic simulation. Despite the number of computer programs dedicated to three-dimensional prediction and surgical planning, maxillofacial practitioners often prefer to rely on conventional two-dimensional methods. This fact could be explained with the overall results given by these methodologies, which seem to be only partially relevant and/or reliable in the practice of daily clinical activity. Concerning the works based on Maxilim, [PERSON] et al. found that this software makes a clinically satisfactory prediction of soft tissue changes, but with an over-prediction of the position of the upper lip [21]; according to [PERSON] et al., its accuracy is influenced by the magnitude of maxillary and mandibular movements, by the age of the patient and by the usage of V-Y closure, and these authors further reported a low predictability of lower and upper lips [22]. Moreover, a general tendency was reported towards an under-prediction in the area around the cheek and chin and towards an over-prediction at the mandibular inferior border [24]. It was suggested that Maximim inaccuracy in the prediction of the lip region might be due to the modelling algorithm, i.e., mass tensor, and might indicate a non-linear response of the upper lip [21]. The study by [PERSON] et al., although not directly focused on soft tissue prediction, reports that inaccuracies in virtual planning are primarily due to the soft-tissue virtual model [25]. With regard to Dolphin software, [PERSON] et al. reported that the ability to predict 3D soft tissue changes was limited, with an accuracy that may be acceptable for linear changes but not for lateral facial points [28]. [PERSON] et al. reported various degrees of accuracy at each soft-tissue landmark in the horizontal and vertical axes; lower lip prediction was the least accurate. A similar result of low prediction accuracy for the lower lip region is reported by [PERSON] et al. [11]. Conversely, two articles affirmed that the statistical differences between Dolphin soft tissue prediction and the real outcome of the patient did not invalidate the software prediction, as it was close to real surgical results [12; 13]. Dolphin 3D imaging uses a landmark-based photographic morphing algorithm that was developed for two-dimensional prediction and then adapted to three-dimensional prediction; this could be an explanation for its imprecisions.