The Assessment of Prediction of Soft Tissue Changes Following the Surgical Correction of Dento-facial Deformities Using Dolphin 3D Software Package.
Conditions: Orthognathic Surgery
Sponsor: Ain Shams University

Date: 29-3-2020 
 
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“The Prediction Accuracy of Dolphin 3D Software for 
Facial Soft Tissue Changes after Bimaxillary 
Orthognathic Surgery” 
 
Protocol submitted to the Faculty of Dentistry Ain-Shams 
University in partial fulfillment of the requirements for The 
Doctor Degree in Oral and Maxillofacial Surgery. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

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Introduction and Review of literature 
During the last decades, there has been tremendous expansion in 
orthognathic surgery for the correction of dentofacial deformities. 
Increasingly sophisticated methods have been designed to improve the 
surgical outcome. At the same time the need to predict the end results has 
grown, and retrospective studies of treated patients have provided the 
clinician with guidelines for estimating the soft tissue response to the 
surgical movements of the osteotomy segments. (1,2) 
Two dimensional planning and prediction methods: 
The  pre-operative planning of orthognathic  surgery, and the 
prediction of the soft tissue changes  became highly desirable(3). The early 
attempts to predict the postoperative profile of patients following 
orthognathic mandibular surgery is to cut a profile photograph to predict 
the postoperative appearance. (4,5) 
The procedure of digitizing the cephalogram point using computer 
programs started in the eighties. The technique includes the digitization of 
the usual cephalometric landmarks by the operator followed by the 
repositioning of the different parts of the jaw bones using a specialized 
computer software programs (6,7). The method is more convenient and 
impresses the patient, but the provided information is no different from the 
first technique. (8)  
However, these studies have focused on the soft-tissue profile as 
presented on lateral cephalogram, and they do not provide sufficient 
information to allow an accurate prediction of the postoperative 
appearance.(8) These Methods were focusing primarily on the lateral 
cephalogram,(9,10) which is certainly important but of a little direct 
concern to the patient. Because the patient is often interested in knowing 

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the details of the expected facial appearance following surgery (10,11) 
video imaging has the potential to radically improve the nature and 
sophistication of the pretreatment communication between the patient and 
the doctor regarding the potential esthetic outcomes of the treatment 
alternatives being considered.(12,13) 
Authors have used video imaging modalities for soft tissue 
prediction which provided  a significant advance in the field of both 
orthodontic and orthognathic prediction(5,6). The basic difference lies in 
the additional step of superimposing the patient’s lateral photograph onto 
the cephalogram (2D computerized prediction). (14,15) the main limitation 
of these systems is that the computerized prediction of the soft tissue 
changes following surgery is limited to the profile which is not suitable for 
the correction of facial asymmetry.   It is important for patients to 
understand that the image produced is a simulation, which may be similar 
to but not identical with their final facial appearance.(6,16) In spite of its 
limitations, this latest method has two major advantages over previous 
techniques.(17) First, the tracing is digitally stored and can be altered more 
readily than with other methods. The second advantage of video imaging 
lies in the improvement of the doctor-patient communication(12,15),  it 
promotes greater understanding and satisfaction with the outcome, as long 
as the patient recognizes that the prediction is only a goal and not a 
guarantee. Sarver et al (15) have found that 89% of a sample of patients 
judged video images to be realistic and thought that the goal was achieved. 
In addition, 83% of patients reported that the prediction helped them to 
take a decision regarding the proposed treatment modality. Finally, 72% 
felt that it also allowed them to be an integral part of the treatment process. 
Similarly, Kiyak and Bell(18) have shown that less than 45% of the patients 
who did not have video images as part of the treatment planning, were 

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satisfied with the outcomes of their surgery. Because of the potential 
impact of video imaging on patient expectations is significant the accuracy 
of what is being shown becomes critical.(19) 
3D Virtual planning and prediction: 
The limitations of the Two-dimensional (2D) computer programs for 
cephalometric measurements and treatment prediction were widely used. 
Through the development of three-dimensional (3D) examination 
techniques and computerized analysis methods (Westermark et al. 2005) 
(20), the opportunity to make 3D computerized predictions of orthognathic 
treatments has arisen (Gateno et al. 2000, Gateno et al. 2003)(21,22)  and 
authors started to compare the accuracy of both methods where the 3d 
virtual planning and prediction showed higher accuracy of the 3d than the 
2d method. In 2017 Bengtsson et al conducted a prospective randomized 
blinded case controlled comparison study on Treatment outcome of 
orthognathic surgery comparing  planning accuracy in computer-assisted 
two- and three dimensional planning techniques, the study showed a 
statistically significant difference between 3D and 2D prediction methods 
with an advantage for the 3D. There are several studies on accuracy of 3D 
planning techniques (Marchetti et al. 2006, Xia et al. 2007, Mazzoni et al. 
2010, Tucker et al. 2010, Aboul-Hosn Centenero and Hernandez-Alfaro 
2012, Zinser et al. 2012, Hsu et al. 2013)(23–29). The majority of these 
studies did not test the prediction only; they also assessed transference of 
planned movements to surgery (occlusal templates) (Xia et al. 2007, 
Tucker et al. 2010, Aboul-Hosn Centenero and Hernandez-Alfaro 2012, 
Zinser et al. 2012, Hsu et al. 2013)(24,26,28–30). Almost all of the 
comparable studies on 3D planning have set the level for linear success at 
<2 mm and showed high success rates (80%−100%) (Marchetti et al. 2006, 
Xia et al. 2007,Mazzoni et al. 2010, Tucker et al. 2010, Hsu et al. 

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2013)(23,24,26,29). Hsu et al (Hsu et al. 2013)(29) presented the largest 
number of studied subjects (n=65) and found a mean linear difference for 
the maxilla of 1.1 mm and for the mandible 1.0 mm so it stepped on the 2D 
limitations with proved higher accuracy. 
 
Soft tissue analysis: 
Facial soft tissue analysis is very useful and mandatory for pre-
surgical planning, post-surgical evaluation, or for the evaluation and 
description of maxillofacial growth. Generally, cephalic radiographs have 
been used to analyze maxillofacial soft tissues (Lines et al., 1978; Ayoub 
et al.,1996)(31,32) , but these two-dimensional (2D) cephalic radiographs 
usually focus on the analysis of hard tissues thereby resulting in limitations 
in the analysis of soft tissues. There are difficulties in reconstructing the 
three-dimensional 
(3D) 
maxillofacial 
form 
and 
performing 
3D 
maxillofacial analysis based on a 2D image. In addition, surgeons wish to 
demonstrate the facial changes and patients also desire to see the facial 
changes via 3D images before and after orthognathic surgery. The 2D 
analysis methods that are currently being used widely such as 
cephalometric analysis do not fulfill these requirements. 
In order to address these shortcomings many studies and clinical 
applications regarding 3D analysis of the cranio-maxillofacial field are 
currently being conducted (Swennen et al., 2006)(33).  
The ideal measurement method for facial morphological analysis 
should have the following characteristics: ability to record facial soft tissue 
data, good accuracy and precision, ability to produce 3D images, and 
reproducibility (Thomson, 1985)(34). It should also have a low technique 
sensitivity and be safe for the patients and the operator, noninvasive, quick 
and easy to perform, and not be too expensive (Miller et al., 2007)(35).  

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The methods of 3D facial soft tissue analysis that are currently being 
widely used in clinical practice include contact techniques represented by 
direct anthropometry (Allanson et al., 1993)(36),and digitizers (de 
Menezes et al., 2009)(37)  and non contact techniques which are 3D laser 
scans (Moss et al., 1994; Bush and Antonyshyn, 1996)(38,39), stereoscopic 
camera (Ayoub et al., 1998; de Menezes et al., 2009)(32,37), and 3D 
computerized tomography (CT) (Moerenhout et al., 2009)(40). Clinicians 
should be aware of the advantages and disadvantages of each 3D facial soft 
tissue analysis method. 
Contact measurements represented by direct anthropometry is the 
most common and easy 3D facial soft tissue measurement method. Facial 
measurement values are obtained by establishing a direct contact between 
the facial surface and the measurement tool such as vernier callipers. 
However, this method requires good patient cooperation since he or she 
needs to stay in a certain position and this is very difficult in children, the 
elderly, or the disabled. The digitizer method also acquires the 
measurement values via establishing a direct contact with the subject The 
reference points are placed in a 3D location of x, y, z coordinates, and the 
distance between two points is calculated to the nearest 0.000001 mm. This 
method is precise, but since there is an inevitable time interval between 
marking the landmarks, it is difficult to apply it in patients who have 
difficulty in staying still. It measures the fixed objects very accurately, and 
hence it is commonly used for performing standard measurements. The 
time taken by the method to perform the measurements is approximately 
the same as that taken by the direct anthropometry method (1 minute). 
Direct anthropometry and the digitizer method need to establish a contact 
with the object (contact measurement methods). 
 

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Noncontact measurement methods include 3D cephalometry using 
3D CT, 3D laser scan, and stereoscopic camera. 3D CT is highly accurate 
and 3D facial reconstruction and recording are possible (Fourie et al., 
2010)(41). Since CT basically provides sectional view of the object, 3D 
images of hard tissues and soft tissues are available in a single CT data set 
(Ono et al., 1992; Moerenhout et al., 2009)(40,42). There are disadvantages 
of this method such as: the cost is high, the equipment is generally not 
portable, and patients are exposed to radiation, the texture and colour of 
tissues cannot be visualized. Cone beam CT is often used because it is 
cheaper than the fan beam CT (conventional), and it can reduce exposure 
to radiation and acquire CT data quickly. The cone beam takes 
approximately 20 seconds to capture the facial image. 3D laser scan uses 
laser to capture the left and right side images at an interval and combines 
them into a 3D image. However, since there was an interval between the 
two scans; breathing motion, facial movement, and the characteristics of 
soft tissues such as the level of tension and fatigue can lead to errors in the 
measurements. Therefore, there can be an increased technical error of 
measurement when it is used in patients with facial problems or disabilities. 
Since a laser scan can demonstrate the texture of the object, but in 
monotone, patients might not identify with the 3D images. It takes 
approximately 30 seconds to capture a facial image by a laser scan. 
 
The stereoscopic camera uses two or more cameras according to the 
principle of binocular vision. ,It takes two or more plain images of the 
subject at different angles and distances, and reconstructs a 3D image from 
plain images (Khambay et al., 2008; Winder et al., 2008; Fourie et al., 
2011)(43–45). This method can capture an image in a short time, and 
demonstrate the colour of the surface. With the development of imaging 
technology, many different types of stereoscopic cameras have been 

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introduced recently. The image capture time of approximately 1 second 
was very short. However, for this method the head has to be placed in an 
appropriate position and angle according to the manufacturer; and some 
time and effort on the part of the operator are needed to use it properly. 
Moreover, CT laser scan, and stereoscopic photography are used for 
providing 3D images and 3D facial measurement values. The other two 
methods such as direct anthropometry and digitizer scan measure the 3D 
facial values, but they cannot produce 3D images. Therefore, it needs to 
determine whether the former three methods provide accurate and 
reproducible measurement values compared to the latter two methods and 
to clarify whether 3D image reconstruction can be used as a diagnostic tool 
or should only be used as a patient consultation tool.  
Kook et al, 2014 (46) compared all the five measurement methods 
and found a high coefficient of reliability, and a high accuracy with a TEM 
(Technical error of measurement) of less than 0.9 mm. These results are 
comparable with those in previous reports (Weinberg et al., 2006)(47) . 
Therefore, it can be stated that all these methods showed good accuracy 
and reproducibility and hence they can be used in research and in clinical 
practice. Although the stereo photogrammetry demonstrated a discrepancy 
compared to the other analysis methods, it was negligible; and therefore, 
3D facial soft tissue analysis methods are expected to be appropriate and 
useful in research and clinical practice.  
Comparing pre-operative and post-operative analyses: 
Assessing the accuracy of 3D surgical predictions compared to the 
actual postoperative result relies on similar techniques of superimposition 
to 2D, but the method of measurement is potentially more complex. To 
date, several methods of analysis have been reported including: (1) 
differences in distance of specific landmarks,(48,49) (2) differences 

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between all the 3D points of the two entire facial surface meshes,(50,51) 
and (3) differences between all the 3D points of the two facial surface 
meshes following division into predefined anatomical regions.(52) 
Quantitative analysis of each technique involves measuring the linear 
distances between specific landmarks or between all of the 3D points of 
the two 3D surface meshes. This can be performed taking into account the 
direction using the average distance difference, i.e. the signed difference, 
or irrespective of direction using the absolute Euclidean difference, and 
finally the root mean square (RMS) difference. The signed differences will 
cancel out positive and negative values, under-estimating the error; the 
absolute Euclidean difference will ignore the direction and only report the 
magnitude. 
Analysis based on surface meshes 
Pre and post-operative image registration for comparison:  
The analysis of 3D surface polygonal meshes is one method of 
assessing topographical and positional surface changes in 3D. To assess 
the accuracy of the software-derived prediction compared to the actual 
final soft tissue image, registration or superimposition of the two images is 
required. This can be achieved using two methods. The first involves 
maintaining the existing hard and soft tissue relationship of each of 
patient’s prediction and actual images and then superimposing the hard 
tissues on the anterior cranial base. As the hard and soft tissue relationship 
is ‘locked’, following hard tissue alignment, the soft tissues will also be 
aligned relative to their hard tissue position. The distance between the two 
soft tissue surfaces can then be analyzed. The second option is to ignore 
the underlying hard tissue and use the forehead region of the soft tissue 
only to align the two images. The results may be different, especially if the 
patient has lost adipose tissue from around the upper face region; this will 

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be immediately apparent if the images are aligned on the skull but not if 
they are aligned on the soft tissue only. Alignment of the images relying 
on the soft tissue only may also be difficult as there is less surface 
topography for the images to reliably align on.  
Following alignment of the images, the distance between the two 
surfaces can be determined. The current method is based on the distance 
between one point on one surface and the closest point to it on the second 
surface. Note that the measurement relies on the closest point, which may 
not necessarily be the corresponding anatomical point. Unless the surface 
meshes are identical, there will be some points on one mesh where the 
closest point on the second mesh is absent, but the algorithm finds an 
inappropriate point on the second mesh and produces a large incorrect 
reading. To overcome the effects of these ‘outliers’, some studies ‘filter-
out’ the values by using the 95th and 90th percentiles.(50–52)  
As the facial surface mesh is made up of thousands of points, an 
overall mean value for the error is often reported. If one facial surface mesh 
is totally in front of the other, all the distance measurements will be either 
positive or negative and therefore the mean is valid. If however some parts 
of one mesh lie behind and some in front of the other mesh, distance 
measurements for the whole face will be made up of positive and negative 
values. Any positive values will cancel out any negative values under- 
estimating the mean difference and so biasing the results. In order to 
overcome this problem, some studies use the absolute mean values or 
Euclidean distances, which ignore the sign (direction) and report on the 
magnitude of error or the RMS error. The RMS error is derived from all 
the points making up the surface mesh which makes it not clinically useful, 
as the site of the error remains unknown; however, dividing the face into 
anatomical regions and analyzing them separately immediately focuses on 

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the areas of error. Many studies assess the accuracy of the prediction by 
comparing the differences in distance between the prediction and the actual 
surfaces using the entire face, including the areas that have been used for 
alignment, i.e. the areas with minimal change. Therefore a large percentage 
of the mesh has minimal or no change; this will bias the accuracy by 
reducing the impact of the error–the data will be skewed towards the 
smaller values. In order to overcome this, areas of the face used for 
alignment can be excluded and the remaining mesh divided into 
anatomically relevant regions this method highlights the true level of error 
in the predictive ability of the package. 
Analysis based on landmarks 
The use of landmarks to assess soft tissue is routine practice in 
orthodontics, especially when analyzing 2D images, i.e. radiographs and 
photographs. Therefore it seems logical to transfer this method from 2D to 
3D. However, this method greatly under-utilizes the 3D data as the 
positional changes of these landmarks describe a change in an isolated 
point not a shape change. The associated error of landmark identification 
will also add to the overall error of this method of analysis and can be over 
2 mm depending on the landmark.1If landmarks are going to be used, the 
data need to be analyzed correctly. To allow comparison, the distance 
between specific landmarks was recorded; the images were aligned to a 
common 3D origin and to one another. To analyze the difference, the 
anatomical point on one mesh is selected and the software calculates the 
distance 90 degrees to the tangent of the point on the second mesh. The 
analyzed point may not be the correct anatomical point on the second mesh, 
but the closest point; the horizontal and vertical components of the 
direction are also unknown. 

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Another option is to select the same anatomical landmark on both 
meshes, extract the 3D coordinates of both points, and use software to 
calculate the Euclidean distance between the points. This requires an extra 
step, but is clinically valid. Some software packages will allow direct 
measurement of the Euclidean distance between the two landmarks. (53) 
 
Dense correspondences and generic conformed facial meshes 
with and without face division into predefined anatomical 
landmarks. 
For long time, the analysis of three-dimensional (3D) facial images 
has generally been limited to linear and angular measurements between 
anatomical landmarks. The operator usually identifies and digitizes a set of 
landmarks that result in a 3D landmark configuration, which is then used 
for analysis. The limited number of accurately identifiable landmarks does 
not allow a comprehensive analysis of the facial morphology. To overcome 
this problem, the concept of a ‘generic mesh’ was introduced.(53) The 
presented innovative approach provides a useful tool for 3D analysis of the 
face; it provides comprehensive evaluation of the morphological 
characteristics, which is superior to assessment at a limited set of individual 
landmarks. A generic mesh can be thought of as a ‘simplified symmetrized 
facial mask’ that contains a known number and distribution of points or 
‘vertices’. The triangles or ‘faces’ formed by these vertices are indexed or 
ordered within the file structure. The generic mesh can be used to 
standardize the number and distribution of vertices for images of the same 
individual and between individuals. Using the process of ‘conformation’, 
the generic facial mesh can be ‘wrapped’ around any facial image 
depending on several anchoring landmarks, whilst the remaining points are 

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mathematically fitted or elastically deformed to maintain the surface 
topography of the original 3D image. The conformation process on the 
preoperative and postoperative 3D facial images produces two meshes, 
which have the same number of vertices and triangles. Each vertex 
represents a corresponding point on the pre- and post-operative conformed 
meshes. The accuracy of the conformation process of the generic facial 
meshes will determine the precision in relating the corresponding facial 
points for the analysis.  
Dense correspondence analysis has been reported as an efficient 
method of analyzing morphological changes, which may explain its broad 
applications in the medical field.(54) However, despite its accuracy and 
comprehensiveness in soft tissue analyses, this approach is largely 
dependent on ‘3D model elastic deformation’, in which the generic facial 
mesh is elastically deformed to reproduce the individual's facial features. 
The initial step of the conformation process involved the translation of the 
corresponding landmarks to match their positions on the target image, 
followed by elastic deformation to minimize the bending energy. This 
process included both shape and positional changes. A total of 15 
landmarks are used to execute the conformation procedure. To eliminate 
bias, these landmarks were excluded from the analysis of the accuracy of 
the conformation procedure. The accuracy of the conformation process has 
been previously reported.(55,56) In these studies, the accuracy was 
determined by measuring the inter-surface distance between the conformed 
mesh and the target models. The disadvantage of this approach is that the 
magnitude of error is measured as the distance between the closest points 
on the two surface meshes, namely the target model and the conformed 
mesh, and not the distances between the actual anatomical corresponding 
points. Measuring the closest distance between two meshes would not 

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necessarily detect the potential sliding of the surface meshes over one 
another during the conformation process, which would provide a 
misleadingly low estimate of the conformation errors. However, the 
assessment of the accuracy of the conformation process based on specific 
landmarks also carries the risk of overestimating the accuracy of the 
conformation process as only a single point on the mesh is analyzed, whilst 
the remainder of the mesh is not assessed. The Euclidian distance between 
the actual landmarks on the non-conformed mesh and the landmarks on the 
conformed generic mesh was used as a measure of accuracy of the 
conformation process. Although this was not a comprehensive surface-
based analysis, its robustness was maximized by carefully selecting the 
landmarks to represent various anatomical regions of the face, which was 
believed to be clinically relevant. The analysis was repeated using the 
classical intersurface distances based on the 90th percentile of the vertices 
of the two meshes and measuring the mean distances between the 
conformed mesh and original mesh for all facial expressions. This measure 
takes into account the direction of error and produces positive and negative 
values, which depend on the spatial location of the meshes relative to each 
other. Despite the fact that these measurements are descriptive to the 
magnitude and the direction of the conformation errors, the mean value of 
these measurements are underestimated as the positive and negative 
measurements would cancel each other. Moreover, the Euclidean distances 
measure the shortest distances between corresponding points on the two 
surface meshes, irrespective of the directionality of the mismatch between 
the two surface meshes; therefore, the arithmetic average value of these 
distances is more meaningful. As expected, the error based on the mean 
absolute distances is much smaller than those based on the Euclidian 
distances. Two main factors may contribute to the errors in the 
conformation process. First, and the most important, is the accuracy and 

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reproducibility of the digitization of the landmarks, which are used in the 
initial conformation stage. This was minimized in the present study through 
pre- landmarking. The second source of errors depends on the deficiency 
in the algorithm of the conformation process.(55) to reduce the effect of 
landmarking errors, which affects the reliability of the conformation 
process, 2-mm-diameter round markers were pre-placed on 34 anatomical 
points on each participant's face. The use of pre-landmark placement 
significantly reduced the landmarking error and allowed the conformation 
process to be analyzed comprehensively by eliminating this potential 
source of error.(57) It can be applied for the evaluation of a sequence of 
3D facial images (4D) for the analysis of the dynamics of facial 
expressions. We expect the method to be fully integrated as a clinical tool 
with various surgical specialties to improve the quality of diagnosis and 
prediction planning of corrective facial surgeries. The limitation associated 
with the visualisation of 3D facial model on a flat screen can be solved with 
the production of 3D objects using the innovation of 3D printing and rapid 
prototyping.(58) Then a distance color map can be generated for the visual 
illustration of the conformation process.   
A generic facial mesh is a digitally constructed surface mesh that has 
the same shape as a typical human face. It consists of a known number of 
triangles and therefore a known number of points or vertices. (55,59) It is 
used to overcome the problem of two 3D surface meshes normally having 
broadly similar shapes but a different number of triangles; making it 
difficult to directly relate one point on one mesh to the same point on the 
other mesh. If the generic mesh is “wrapped” around two different 3D 
facial images, each new generic mesh will have the shape of each of the 
original 3D images and both new generic meshes will now have the same 
number of triangles and vertices. Since a point on one generic mesh is the 

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same point on the other, direct anatomical correspondence can be achieved. 
The application of generic surface meshes allows comprehensive analysis 
using “dense correspondence analysis” of 3D human facial images using 
all the point making up the generic mesh pro- viding a comprehensive 
quantitative evaluation of the examined surfaces(60). 
 
Softwares prediction different computational strategies 
The orthognathic planning softwares use several computational 
strategies (models) to generate soft tissue predictions based on the bone-
related planning. These four strategies are: a linear Finite Element Model 
(FEM), a non-linear Finite Element Model (NFEM), a Mass Spring Model 
(MSM) and a novel Mass Tensor Model (MTM) Molleman’s et al For true 
validation of these four models we acquired a data set of 10 patients who 
underwent maxillofacial surgery, including pre-operative and post-
operative CT data. For all patient data we compared in a quantitative 
validation the predicted facial outlook, obtained with one of the four 
computational models, with post-operative image data. During this 
quantitative validation distance measurements between corresponding 
points of the predicted and the actual post-operative facial skin surface, are 
quantified and visualized in 3D. The results showed that the MTM and 
linear FEM predictions achieve the highest accuracy. Furthermore, the 
MTM turned out to be the fastest model, with an average simulation time 
of only 10 s. Besides this quantitative validation, a qualitative validation 
study was carried out by eight maxillofacial surgeons, who scored the 
visualized predicted facial appearance by means of predefined statements. 
This study confirmed the positive results of the quantitative study, so we 
can conclude that fast and accurate predictions of the post-operative facial 
outcome are possible. Therefore, the usage of a maxillofacial soft tissue 

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prediction system is relevant and suitable for daily clinical practice. Our 
results show that the MTM achieves the same accuracy as the linear FEM, 
but clearly beats MSM and the linear FEM in simulation time. No 
significant improvement of the simulation accuracy was found, when using 
the non- linear FEM. The validation study denotes moreover an acceptable 
error of the predictions for the MTM and linear FEM when quantitatively 
measuring distances between the predicted and real post-operative facial 
outlook. These positive results were confirmed by the qualitative validation 
study, so that we are able to conclude that maxillofacial soft tissue 
prediction systems can be used in daily clinical practice. (3) 
The 3D color maps permit an objective analysis of the craniofacial 
structures. The method facilitated the evaluation of orthognathic 
surgery(61), reconstructive surgery(62),orthodontics(1), growth(63), and 
creation of facial templates(64). A 3D color map is a graphical qualitative 
and quantitative representation of the distance differences between 2 
superimposed 3D images. Currently, most of the software compatible with 
3D imaging data has the capability of generating color maps.(64) Color 
maps provide a qualitative methods of visualizing quantitative changes in 
skeletal position(63).  
In the current study the research team try to formulate a complete 
surgical planning and prediction protocol that can pair the usage of recent 
methods of surgical planning, prediction, and assessment with the most 
reasonably priced service to allow treatment of patients with class II facial 
deformity in our developing country. 
 
 
 

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The Aim 
The aim of the present study is to evaluate the accuracy of Dolphin 
3D software in the prediction of facial soft tissue changes after 
bimaxillary orthognathic surgery. 
 
Study Hypothesis 
 
There will be no difference between actual soft tissue results and 
Dolphin 3D software soft tissue prediction. 
 
 
 
 
 
 
 
 
 
 
 
 
 

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Patients and Methods 
 
This will be a retrospective study to investigate the accuracy of 
Dolphin 3d software for the prediction of post-operative soft tissue changes 
following bimaxillary orthognathic surgery to correct class III jaw defects 
through combined maxillary advancement and mandibular setback. The 
study will employ the pre-existing CBCT images routinely captured for 
orthognathic surgery patients in Glasgow dental hospital and school. Then 
difference between the pre-operative soft tissue prediction and the 6-month 
postoperative results will be analyzed. The study will be conducted in 
Glasgow dental school and hospital using patients pre-existing data after 
the ethical approval.  
 
 Sample size calculation  
Based upon the assumption that we need to reach 90% accuracy, in light of 
the results of Resnick CM et al (2016), the computed effect size for the 
accuracy of linear measurements was found to be (1.05), using alpha (α) 
level of (5%) and Beta (β) level of (20%) i.e. power = 80%; the study will 
include 10 subjects. To compensate for 15% for the use of non-parametric 
tests; the final minimum estimated sample size will be 12 subjects. Sample 
size calculation was performed using G*Power Version 3.1.9.2. 
 
Patients to be included: 
a) Patients who have class III facial deformity to be corrected using 
bimaxillary orthognathic surgery. 
b) No medical problems that might interfere with general anesthesia or 
interfere with bone and soft tissue healing. 

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Patients will be excluded if: 
a) There are craniofacial anomalies including cleft lip/palate,  
b) Patient have previous maxillofacial operations or facial scars, 
c) Correction mandates multi-segment Le Fort I osteotomies,  
d) There are orthodontic appliances in place at the time of T1 records. 
e) Genioplasty is recommended for deformity correction. 
f)  There is facial Asymmetry. 
 
All the patients are consented for using their data in research purposes. 
The CBCT scans of class III patients who undergone maxillary 
advancement with minimal or without impaction and mandibular setback 
will be identified. The preoperative and 6 months postoperative CBCT 
images will be anonymized and will be saved in a password protected 
computer. Only the research team members will have access to the research 
data.  
 
In order to quantify the actual amount of surgical movement, the pre 
and post-operation CBCT voxel based super imposition using 
ONDEMAND 3d. The surgical movement will be quantified using direct 
slice land-marking.  
 
Using the pre-operation CBCT to do the surgical mock up and the soft 
tissue simulation on dolphin 3D. Soft tissue surface models of the 
simulation and the actual postoperative images will be generated and 
exported as STLs.  
Data Analysis 
 
Superimposition of the predicted and the post-operative STLs and 
conformation of the generic meshes will be performed. Using VR mesh 
software, the mesh will be segmented into pre-identified anatomical 
regions. The distance between the corresponding vertices will be displayed 

Date: 29-3-2020 
 
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as a colour scale in milli-meter and will be analyzed in the individual 
dimension of space (x, y and z) using in-house software developed for this 
purpose and each dimension will be displayed as a colour coded distance 
map. It will be generated to illustrate the magnitude and anatomical regions 
with prediction inaccuracies analysis. Using this software, the minimum, 
maximum, mean, standard deviation (SD), absolute maximum, absolute 
mean, and absolute SD of 90% of the points of each facial anatomical 
region will be measured. 
Statistical analysis 
Numerical data will be explored for normality by checking the 
distribution of data and using tests of normality (Kolmogorov-Smirnov and 
Shapiro-Wilk tests). Data will be presented as mean, median, standard 
deviation (SD), minimum, maximum and 95% Confidence Interval (95% 
CI) for the mean values. For parametric data, paired t-test will be used to 
compare between actual soft tissue measurement and software 
measurement. For non-parametric data, Wilcoxon signed-rank test will be 
used to compare between actual soft tissue measurement and software 
measurement. Inter- and intra-examiner reliability will be assessed using 
Cronbach’s alpha and Intra-Class Correlation coefficients. 
The significance level will be set at P ≤ 0.05. Statistical analysis will 
be performed with IBM® SPSS® Statistics Version 20 for Windows. 
 
 
 
                                                             
® IBM Corporation, NY, USA. 
 
® SPSS, Inc., an IBM Company. 
 

Date: 29-3-2020 
 
22 
 
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