# 3D Soft-Tissue Prediction Methodologies for Orthognathic Surgery--A Literature Review
## 3 Results
### Prediction Methodologies
#### 3.2.3 Works Based on Other 3D Software  
The work of [PERSON] et al. investigates the accuracy of Dolphin 3D soft tissue prediction using seven patients who had a single-segment Le Fort I osteotomy. Dolphin 3D Imaging (Dolphin Imaging and Management Solutions, Chatsworth, CA, USA) is based on a landmark photographic morphing algorithm developed for 2D prediction and adapted to 3D. The user must locate 79 landmarks, 47 on the hard tissue and 37 on the soft tissue, on the CT volume; the system generates adaptable curves between the landmarks, similarly to the tracing of a lateral cephalometry. On the pre-operative segments LF I osteotomy was simulated by Dolphin, then hard and soft tissue landmarks were assigned, morphing curves were adjusted and finally prediction image (Tp) and post-operative image (T1) were aligned and registered for the measurement of the differences. For each patient, the error was calculated as the difference occurring between Tp and T1 at 14 points, six on the midline and eight laterally and at the nasolabial angle (Figure 4). Twelve of these points are standard anthropometric or cephalometric landmarks. Two new points were introduced for the study: lateral aa (LA), obtained from the intersection of the line tangent to endocanthion (EN) and the one tangent to subalare (SBAL), and maxillary buttress (MB), traced at the intersection of lines tangent to exocanthion (EX) and SBAL (Figure 4A). In closing, their analysis showed that the capability to predict the soft-tissue changes in the three dimensions after LF I surgery using Dolphin software had some limitations. For linear changes, its accuracy was acceptable, while it was not acceptable for lateral points of the face. Concerning the midline, changes at nasal base were more subject to prediction errors [28]. Figure 4: Points for measurement by [PERSON] et al. (**A**) Frontal view. The two points derived for this study are shown: lateral ala point (LA), defined as the intersection of lines tangent to endocanthion (EN) and subnasale (SBAL), and maxillary buttress (MB) point, and defined as the intersection of lines tangent to exocanthion (EX) and SBAL. (**B**) Lateral view showing all points [28]. [PERSON] et al. [29] and [PERSON] et al. [30] worked on SurgiCase_CMF using CBCT and MSCT respectively of 10 patients with facial deformities. In the study presented by [PERSON] et al., patients underwent LFI, BSSO and genioplasty. The pre- and post-operative CT images were aligned on the top of each other. Firstly, a registration point was made, with the location of two corresponding landmarks on pre- and post-operative conditions. After the manual registration, automatic surface registration was carried out using an iterative closest point algorithm. A crucial point in surfaces registration was to indicate those that did not change after the surgery; authors used the eyes area and the forehead as fixed regions. Finally, the post-operative soft tissue surface was compared with the software virtual simulation. A comparison algorithm was used to measure the distance of every triangle corner of the post-operative surface against the preoperative one. The percentage of error was <2.0 mm for the 86.80% of patients, even if there were important errors in the top lip and chin regions. The results in the group of patients studied with CBCT (which had a reliability of 86.8%) stated that the use of SurgiCase_CMF 1.2 software combined with CBCT data allowed to achieve a realistic preview with low X-ray exposure [29]. [PERSON] et al. divided virtual surgical planning in four steps (Figure 5): (1) CT data reconstruction; (2) generation of 3D models of hard and soft tissues; (3) various virtual surgical planning and simulation mode; (4) different pre-operative previews of the soft tissues. Skeletal model surgical planning and simulation were performed on the base of the 3D CT; the soft tissue model was made through a physical model.