# Evaluation of Deviations Produced by Soft Tissue Fitting in Virtually Planned Orthopathic Surgery
## 4 Discussion  
Nowadays, it is clear that digital tools are useful for surgical planning and development. In fact, the ability to 3D-print personalized occlusion splints designed using CAD techniques has significantly reduced errors relating to inter-jaw positioning [29]. This translates into higher satisfaction rates and better functional outcomes from procedures. Meanwhile, complete virtual planning is becoming more common in procedure planning. However, the scientific community is still divided on its validation as a step forward for orthognathic surgery [30]. Our results align with previous reports and reviews indicating that VSP does not accurately predict surgery and that deviations from the plan are necessary [31]. While many studies agree that VSP accurately predicts hard tissue, soft tissue planning still requires workarounds and presents problems in the lower face, upper lip, and nasal base [24; 32]. This is all related to the Class II phenomena observed when predicting mandibular soft tissue fitting, which could be connected to the OS soft tissue prediction evaluations where Dolphin software was included. While Dolphin works well with cephalometric radiographs, its modelling of 3D tissues is limited. Studies have shown that Dolphin exhibits greater variability in performance (1.8 \(\pm\) 0.8 mm) than ProPlan and PFEM (\(\sim\)1.3 \(\pm\) 0.4 mm), indicating slightly lower performance in 3D predictions. Other software, such as IPS CaseDesigner, exhibits variability similar to that of Dolphin, empha  
Figure 3: Mean OQOL scores and SDs for each questionary domain and total scores summarized. Dots represent individual score values. (**A**) Representation of OQOL results for all study patients. (**B**) Representation of OQOL results for all study patients grouped by VSP following. (**C**) Representation of OQOL results for Class II patients grouped by VSP following. (**D**) Representation of OQOL results for Class III patients grouped by VSP following. “On VSP” represents those patients for whom VSP was followed; “Mod. VSP” represents those patients for whom the surgeon decided to modify the positioning from VSP. sizing variability in predicting the lower lip. In addition, PFEM offers a more complex, parametrizable simulation, but its technical complexity increases significantly. However, issues regarding sample size should be addressed, as these are common problems in these studies [24; 26].PFEM is aligned with newer approaches involving more complex finite element modelling (FEM) and must be considered for extension to other VSP software. In some studies, complex FEM modelling based on a combination of CT and MR data has shown deviations of 0.55 mm in the midface area between simulation and postoperative CBCT. However, high variability (2.9 mm) has also been demonstrated. Once again, sample size limitations are a key issue for these studies [25]. Novel sliding FEM models have been proposed for retrospective data, showing promising results, especially for predicting the lips and mucosa, but these have not yet been applied to real surgical VSP procedures. [33]. The studies summarized in Table 4 had a similar sample size to those using real surgical application data and shared the overall deviation observed for [PERSON]'s VSP surgeries. However, none of them studied differences between dentofacial deformity classes or surgical functional outcomes using OQOL or other methods. The fact that cranial areas can be simulated more accurately than lower facial areas may explain the differences observed in our study between Class II and Class III patients with respect to VSP compliance [24]. Class II dentofacial deformity (DFD) correction typically requires more mandibular advancement than Class III, resulting in poorer soft tissue simulation due to greater hard tissue displacement carrying soft tissue movement. While this explains our findings, there is no literature on performance observations by dentofacial deformity classes.