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This study aimed to evaluate the accuracy of the Digital Imaging software in the prediction of soft tissue changes following three types of orthodontic interventions: non-extraction, extraction, and orthognathic surgery treatments. Ninety-six patients were randomly selected from the records of three orthodontic interventions (32 subjects per group): (1) non-extraction, (2) extraction, and (3) orthodontic treatment combined with orthognathic surgery. The cephalometric analysis of soft tissue changes in both the actual post-treatment and the predicted treatment was performed using Dolphin Imaging software version 11.9. A paired t-test was utilized to assess the statistically significant differences between the predicted and actual treatment outcomes of the parameters (p < 0.05). In the non-extraction group, prediction errors were exhibited only in the lower lip parameters. In the extraction group, prediction errors were observed in both the upper and lower lip parameters. In the orthognathic surgery group, prediction errors were identified in chin thickness, facial contour angle, and upper and lower lip parameters (p < 0.05). Digital Imaging software exhibited inaccurate soft tissue prediction of 0.3–1.0 mm in some parameters of all treatment groups, which should be considered regarding the application of Dolphin Imaging software in orthodontic treatment planning. [ABSTRACT FROM AUTHOR]

This study aimed to evaluate the accuracy of the Digital Imaging software in the prediction of soft tissue changes following three types of orthodontic interventions: non-extraction, extraction, and orthognathic surgery treatments. Ninety-six patients were randomly selected from the records of three orthodontic interventions (32 subjects per group): (1) non-extraction, (2) extraction, and (3) orthodontic treatment combined with orthognathic surgery. The cephalometric analysis of soft tissue changes in both the actual post-treatment and the predicted treatment was performed using Dolphin Imaging software version 11.9. A paired t-test was utilized to assess the statistically significant differences between the predicted and actual treatment outcomes of the parameters (p < 0.05). In the non-extraction group, prediction errors were exhibited only in the lower lip parameters. In the extraction group, prediction errors were observed in both the upper and lower lip parameters. In the orthognathic surgery group, prediction errors were identified in chin thickness, facial contour angle, and upper and lower lip parameters (p < 0.05). Digital Imaging software exhibited inaccurate soft tissue prediction of 0.3–1.0 mm in some parameters of all treatment groups, which should be considered regarding the application of Dolphin Imaging software in orthodontic treatment planning.

The 3D prediction of post-operative changes is an inevitable tool for the surgical correction of facial asymmetry. The objective is to execute an evidence-based review answering the following question. Does the 3D virtual prediction planning draw reliable and accurate results in the surgical outcome related to the soft tissues of the face in facial asymmetry? This systematic review of the literature is based on the 3D soft tissue prediction planning of facial asymmetry correction to draw conclusions on the reliability and accuracy of these methods in the surgical outcome related to the soft tissues of the face. PubMed, Web of Science, Cochrane, and Ovid databases were adopted for the literature search. Studies published between years 2000 and 2020, aimed at the assessment of soft tissue predictions using software prediction packages for facial asymmetry, were selected. The Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) was applied. Quadas-2 tool was used for the qualitative evaluation of selected studies. Initial search yielded 248 articles. Twenty articles fulfilled the inclusion and exclusion criteria and selected for qualitative analysis. Finally, 12 articles were selected for quantitative analysis. The results indicate 3D imaging prediction methods provided more accurate information with less distortion for soft tissue prediction regardless of various softwares currently available. The prediction of soft tissue accuracy in facial asymmetry was less accurate in lower face regardless of the type of surgery for facial asymmetry. The mean prediction error was less than 2 mm. [ABSTRACT FROM AUTHOR]

Using a computer-aided, three-dimensional surgical simulation planning program, soft-tissue planning can help achieve adequate facial esthetics and patient satisfaction after orthognathic surgery. This study aimed to assess the Simplant O&O software's soft tissue prediction accuracy. Fourteen skeletal type III patients who underwent orthognathic surgery by the same surgeons were included in this prospective study, and they were separated into two groups: the one-jaw (n = 5) and two-jaw (n = 9) groups. The software was used to analyze the preoperative (T0) and 4-month postoperative computed tomography data (T1), as well as intraoral scans. Data from cone-beam computed tomography and stereolithography from a scanned dental cast were used to reconstruct a composite skull model. Based on the presurgical CT data, the program generated a predicted soft tissue image (TP), which was then superimposed on the T1. The distances between seven T1 and TP landmarks were measured and evaluated using a one-sample t-test. In the one-jaw group, the mean error for all linear measurements was 1.73 ± 1.14 mm, whereas the mean error of the two-jaw group was 1.03 ± 0.83 mm, and both measurements were within clinically acceptable limits. Pronasele had the best correlation (mean error of 0.63 ± 0.45 mm) while soft tissue pogonion and soft tissue point B had the worst correlations (mean error of 2.87 ± 2.22 mm and 1.31 ± 0.98 mm, respectively). Even though there were some limitations, it was possible to conclude that the ability to accurately predict soft tissue changes using Simplant O&O for skeletal type III patients makes it adequate for use in clinical practice. [ABSTRACT FROM AUTHOR]

Objectives: This study aimed to evaluate the soft tissue prediction accuracy of patients undergoing orthognathic surgery to correct skeletal class III malocclusion using maxillofacial regional aesthetic units. Materials and methods: Pre- and postoperative cone-beam computed tomography (CBCT) and 3D facial scans were taken for 58 patients who had undergone orthognathic surgery. The preoperative 3D facial scan was integrated with the preoperative CBCT using ProPlan CMF software. The software simulated the surgery and generated postoperative soft tissue prediction. The simulated 3D facial scan was then compared with the actual 3D facial scan obtained at least 6 months after the surgery by the maxillofacial regional aesthetic units and the facial soft tissue landmark points. Results: The anatomical regions of the upper lip, lower lip, chin, right external buccal and left external buccal prediction were above 2.0 mm. As for the soft tissue landmarks, at chl, chr, ls, stm and li, the position of predicted scan was higher than that of the actual postoperative scan. Conclusions: The accuracy of 3D soft tissue predictions using ProPlan CMF software in Skeletal III patients was clinically satisfactory according to maxillofacial regional aesthetic units combined with facial soft tissue landmark points. However, the accuracy of prediction still needed improvement in some areas. Clinical relevance: The accuracy of soft tissue prediction can be analyzed more clearly through maxillofacial regional aesthetic units so that clinicians have a deeper understanding of the use of the software to predict soft tissue change after orthognathic surgery. [ABSTRACT FROM AUTHOR]

With the increase of the adult orthodontic population, there is a need for an accurate and evidence-based prediction of the posttreatment face in 3 dimensions (3D). The objectives of this study are 1) to develop a 3D postorthodontic face prediction method based on a deep learning network using the patient-specific factors and orthodontic treatment conditions and 2) to validate the accuracy and clinical usability of the proposed method. Paired sets (n = 268) of pretreatment (T1) and posttreatment (T2) cone-beam computed tomography (CBCT) of adult patients were trained with a conditional generative adversarial network to generate 3D posttreatment facial data based on the patient's gender, age, and the changes of upper (ΔU1) and lower incisor position (ΔL1) as input. The accuracy was calculated with prediction error and mean absolute distances between real T2 (T2) and predicted T2 (PT2) near 6 perioral landmark regions, as well as percentage of prediction error less than 2 mm using test sets (n = 44). For qualitative evaluation, an online survey was conducted with experienced orthodontists as panels (n = 56). Overall, PT2 indicated similar 3D changes to the T2 face, with the most apparent changes simulated in the perioral regions. The mean prediction error was 1.2 ± 1.01 mm with 80.8% accuracy. More than 50% of the experienced orthodontists were unable to distinguish between real and predicted images. In this study, we proposed a valid 3D postorthodontic face prediction method by applying a deep learning algorithm trained with CBCT data sets. [ABSTRACT FROM AUTHOR]

Using a computer-aided, three-dimensional surgical simulation planning program, soft-tissue planning can help achieve adequate facial esthetics and patient satisfaction after orthognathic surgery. This study aimed to assess the Simplant O&O software’s soft tissue prediction accuracy. Fourteen skeletal type III patients who underwent orthognathic surgery by the same surgeons were included in this prospective study, and they were separated into two groups: the one-jaw (n = 5) and two-jaw (n = 9) groups. The software was used to analyze the preoperative (T0) and 4-month postoperative computed tomography data (T1), as well as intraoral scans. Data from cone-beam computed tomography and stereolithography from a scanned dental cast were used to reconstruct a composite skull model. Based on the presurgical CT data, the program generated a predicted soft tissue image (TP), which was then superimposed on the T1. The distances between seven T1 and TP landmarks were measured and evaluated using a one-sample t-test. In the one-jaw group, the mean error for all linear measurements was 1.73 ± 1.14 mm, whereas the mean error of the two-jaw group was 1.03 ± 0.83 mm, and both measurements were within clinically acceptable limits. Pronasele had the best correlation (mean error of 0.63 ± 0.45 mm) while soft tissue pogonion and soft tissue point B had the worst correlations (mean error of 2.87 ± 2.22 mm and 1.31 ± 0.98 mm, respectively). Even though there were some limitations, it was possible to conclude that the ability to accurately predict soft tissue changes using Simplant O&O for skeletal type III patients makes it adequate for use in clinical practice.

Purpose: To evaluate the accuracy of three-dimensional (3D) soft tissue prediction in bimaxillary orthognathic surgery. Methods: Cone-beam computed tomographs of 88 patients with class II (n = 46) and class III (n = 42) malocclusions, who underwent bimaxillary orthognathic surgery, were included in this retrospective study. 3D soft tissue prediction and postoperative outcome were compared by using ten landmarks of facial soft tissues. Patients' sex and age were also assessed. Results were analyzed using a mixed model methodology (p < 0.05). Results: The success criterion adopted was a mean discrepancy of < 2 mm. Most mandibular landmarks indicated a tendency for underprediction with a downward direction in class II patients, with some values > 2 mm. In class III, there was overprediction with a downward direction for the mandibular landmarks, with values < 2 mm. More accurate results were found in female and older patients. Conclusions: 3D surgical planning showed clinically acceptable results for predicting soft tissues in patients undergoing bimaxillary orthognathic surgery, with more accurate results for class III patients. Although some differences were found when age and sex were interacted, a consistent association between these variables could not be stated. These results support the clinician, as accuracy can provide a strong guide to the surgeon when planning surgical orthodontic treatment. [ABSTRACT FROM AUTHOR]

The study aimed at developing a deep-learning (DL)-based algorithm to predict the virtual soft tissue profile after mandibular advancement surgery, and to compare its accuracy with the mass tensor model (MTM). Subjects who underwent mandibular advancement surgery were enrolled and divided into a training group and a test group. The DL model was trained using 3D photographs and CBCT data based on surgically achieved mandibular displacements (training group). Soft tissue simulations generated by DL and MTM based on the actual surgical jaw movements (test group) were compared with soft-tissue profiles on postoperative 3D photographs using distance mapping in terms of mean absolute error in the lower face, lower lip, and chin regions. 133 subjects were included — 119 in the training group and 14 in the test group. The mean absolute error for DL-based simulations of the lower face region was 1.0 ± 0.6 mm and was significantly lower (p = 0.02) compared with MTM-based simulations (1.5 ± 0.5 mm). The DL-based algorithm can predict 3D soft tissue profiles following mandibular advancement surgery. With a clinically acceptable mean absolute error. Therefore, it seems to be a relevant option for soft tissue prediction in orthognathic surgery. Therefore, it seems to be a relevant options. [ABSTRACT FROM AUTHOR]

The methods for pronasale prediction proposed by Stephan et al. (2003), Rynn et al. (2010), Lee et al. (2014) and Utsuno et al. (2016) were tested in order to find applicable methods for a Thai population. Cone-beam computed tomography data from 100 adult subjects were analysed, using two- and three-dimensional images. The data were categorized into females, males and regardless of sex groups. Differences between the predicted and actual coordinates were compared using the paired t-test and mean absolute errors (MAEs). Differences between each prediction methods were compared using the two-way ANOVA with a balanced block design or Friedman tests. Our results suggest that the preferable methods for pronasale prediction in the female and male groups are the methods of Utsuno et al. and Lee et al, respectively. The mean and MAE for pronasale projection and height, calculated by the method of Utsuno et al. in females were 0.4(1.9) and −0.4 (1.5) mm. Those value calculated by the method of Lee et al. in males were −0.7(1.9), 1.0 (1.9) mm. Our result can help to select the suitable prediction methods applying for nasal tip prediction for facial reconstruction in a Thai population. [ABSTRACT FROM AUTHOR]

Three-dimensional technologies have had a wide diffusion in several fields of application throughout the last decades; medicine is no exception and the interest in their introduction in clinical applications has grown with the refinement of such technologies. We focus on the application of 3D methodologies in maxillofacial surgery, where they can give concrete support in surgical planning and in the prediction of involuntary facial soft-tissue changes after planned bony repositioning. The purpose of this literature review is to offer a panorama of the existing prediction methods and software with a comparison of their reliability and to propose a series of still pending issues. Various software are available for surgical planning and for the prediction of tissue displacements, but their reliability is still an unknown variable in respect of the accuracy needed by surgeons. Maxilim, Dolphin and other common planning software provide a realistic result, but with some inaccuracies in specific areas of the face; it also is not totally clear how the prediction is obtained by the software and what is the theoretical model they are based on. [ABSTRACT FROM AUTHOR]

Abstract Three-dimensional surgical planning is used widely in orthognathic surgery. Although numerous computer programs exist, the accuracy of soft tissue prediction remains uncertain. The purpose of this study was to compare the prediction accuracy of Dolphin, ProPlan CMF, and a probabilistic finite element method (PFEM). Seven patients (mean age 18 years; five female) who had undergone Le Fort I osteotomy with preoperative and 1-year postoperative cone beam computed tomography (CBCT) were included. The three programs were used for soft tissue prediction using planned and postoperative maxillary position, and these were compared to postoperative CBCT. Accurate predictions were obtained with each program, indicated by root mean square distances: RMS Dolphin = 1.8 ± 0.8 mm, RMS ProPlan = 1.2 ± 0.4 mm, and RMS PFEM = 1.3 ± 0.4 mm. Dolphin utilizes a landmark-based algorithm allowing for patient-specific bone-to-soft tissue ratios, which works well for cephalometric radiographs but has limited three-dimensional accuracy, whilst ProPlan and PFEM provide better three-dimensional predictions with continuous displacements. Patient or population-specific material properties can be defined in PFEM, while no soft tissue parameters are adjustable in ProPlan. Important clinical considerations are the topological differences between predictions due to the three algorithms, the non-negligible influence of the mismatch between planned and postoperative maxillary position, and the learning curve associated with sophisticated programs like PFEM. [ABSTRACT FROM AUTHOR]

Abstract Purpose The purpose of this study was to provide a quantitative accuracy assessment of soft tissue predictions generated by a computer-aided maxillofacial planning system in patients undergoing orthognathic surgery following the “surgery-first” treatment. Materials and methods For this study, we looked at 16 patients with open bite dentofacial-dysmorphosis who underwent orthognathic surgery. Surgeries were planned using conventional sketches and the newly developed computer-assisted SOTIRIOS planning software (developed by the authors). Validation procedures were performed in the following steps: (1) Standardized registration of the pre- and postoperative CT volumes; (2) Automated adjustment of the bone-related preoperative planning to the actual postoperative bony displacement; (3) Simulation of soft tissue changes according to the definitive bony movements; and (4) Calculation of soft tissue differences between the predicted and the actual 6-month postoperative results by distance mapping. Results The program produced a clinically satisfactory 3D soft tissue prediction, with a mean error of 1.46 mm ± 1.53 mm. The program was suitable for use in virtual surgical planning without technical assistance. Conclusion This study shows that the program is quite accurate, enabling the surgeon to predict the outcome of the soft tissue. This has the potential to promote the routine application of the surgery-first approach in patients suffering from open bite. [ABSTRACT FROM AUTHOR]

Three-dimensional technologies have had a wide diffusion in several fields of application throughout the last decades; medicine is no exception and the interest in their introduction in clinical applications has grown with the refinement of such technologies. We focus on the application of 3D methodologies in maxillofacial surgery, where they can give concrete support in surgical planning and in the prediction of involuntary facial soft-tissue changes after planned bony repositioning. The purpose of this literature review is to offer a panorama of the existing prediction methods and software with a comparison of their reliability and to propose a series of still pending issues. Various software are available for surgical planning and for the prediction of tissue displacements, but their reliability is still an unknown variable in respect of the accuracy needed by surgeons. Maxilim, Dolphin and other common planning software provide a realistic result, but with some inaccuracies in specific areas of the face; it also is not totally clear how the prediction is obtained by the software and what is the theoretical model they are based on.

Because of the potential morbidity and complications associated with surgical procedures, limiting the extent of orthognathic surgery is a desire for many orthodontic patients. An eighteen-year-old woman had a severe Class 111 malocclusion and required bi-maxillary surgery. By changing the patient's maxillary occlusal plane using orthodontic mini-implants, she was able to avoid the maxillary surgery; requiring only a mandibular setback surgery. To accurately predict the post-surgery outcome, we applied a new soft tissue prediction method. We were able to follow and report the long-term result of her combined orthodontic and orthognathic treatment. The changes to her occlusal plane continue to appear stable over 6 years later. [ABSTRACT FROM AUTHOR]

Aim: The aim of this study was to evaluate the accuracy of 3D soft tissue predictions generated by a computer-aided maxillofacial planning system in patients undergoing orthognathic surgery. Methods and Materials: Twenty patients with dentofacial dysmorphosis were treated with orthognathic surgery after a preoperative orthodontic treatment. Fourteen patients had an Angle Class II malocclusion; three patients had an Angle class III malocclusion, and three patients had an Angle Class I malocclusion. Skeletal asymmetry was observed in six patient. The surgeries were planned using the Maxilim software. Computer assisted surgical planning was transferred to the patient by digitally generated splints. The validation procedures were performed in the following steps: (1) Standardized registration of the pre- and postoperative Cone Beam CT volumes; (2) Automated adjustment of the bone-related planning to the actual operative bony displacement; (3) Simulation of soft tissue changes; (4) Calculation of the soft tissue differences between the predicted and the postoperative results by distance mapping. Statistical Analysis and Results: Eighty four percent of the mapped distances between the predicted and actual postoperative results measured between -2 mm and +2 mm. The mean absolute linear measurements between the predicted and actual postoperative surface was 1.18. Our study shows the overall prediction was dependent on neither the surgical procedures nor the dentofacial deformity type. Conclusion: Despite some shortcomings in the prediction of the final position of the lower lip and cheek area, this software promises a clinically acceptable soft tissue prediction for orthognathic surgical procedures. [ABSTRACT FROM AUTHOR]

This study examines two recently proposed methods for predicting nose projection from the skull, those developed by Stephan et al. (Am J Phys Anthropol 122, 2003, 240) and Rynn et al. (Forensic Sci Med Pathol 6, 2010, 20). A sample of 86 lateral head cephalograms of adult subjects from Central Europe was measured, and the actual and predicted dimensions were compared. Regarding nose projection (the anterior and vertical position of the pronasale), in general, the method of Rynn et al. (Forensic Sci Med Pathol 6, 2010, 20) was found to perform better and with less error variance than the method of Stephan et al. (Am J Phys Anthropol 122, 2003, 240), but the mean difference between the actual and predicted values did not exceed 2.2 mm (6.5% of the actual dimension) in most of the variables tested. The vertical dimensions of the nose were predictable with greater accuracy than the horizontal dimensions. Although the mean error of both methods is not great in practice and thus both methods could be considered to be 'accurate,' the real variance of error should not be overlooked. [ABSTRACT FROM AUTHOR]

Abstract: Introduction: The management of post-traumatic deformity in the midface region poses challenges for the maxillofacial surgeon. Ensuring symmetry after zygomatic osteotomy can be difficult and precise positioning of the osteotomised bony fragments requires careful treatment planning. It may be necessary to use a coronal flap to allow the surgeon to compare the contralateral zygomatic bone to allow symmetrical reduction. The authors present a new technique for the positioning of osteotomised zygomatic bones using a combination of computer assisted surgical simulation and rapid prototyping. Method: A patient presented to our unit with a post-traumatic zygomatic deformity. Using surgical simulation software the displaced zygomatic bone was osteotomised and placed in the idéal position on a three-dimensional computed tomography scan (3D CT). The position was determined by reference to the contralateral zygoma. In addition the repositioning of the soft tissues was simulated. A surgical guide which allowed intraoperative positioning of the osteotomised zygoma was manufactured by a rapid prototyping process. Use of the guide allowed a minimally invasive approach to the affected zygoma. The post-operative results were compared to the predicted outcome. Results: The post-operative appearance was satisfactory and corresponded well with the predicted result. There was a significant reduction in operative time compared to the previous management of similar cases. [Copyright &y& Elsevier]

Cranio-maxillofacial (CMF) surgery operations are associated with rearrangement of facial hard and soft tissues, leading to dramatic changes in facial geometry. Often, correction of the aesthetical patient's appearance is the primary objective of the surgical intervention. Due to the complexity of the facial anatomy and the biomechanical behaviour of soft tissues, the result of the surgical impact cannot always be predicted on the basis of surgeon's intuition and experience alone. Computational modelling of soft tissue outcome using individual tomographic data and consistent numerical simulation of soft tissue mechanics can provide valuable information for surgeons during the planning stage. In this article, we present a general framework for computer-assisted planning of CMF surgery interventions that is based on the reconstruction of patient's anatomy from 3D computer tomography images and finite element analysis of soft tissue deformations. Examples from our clinical case studies that deal with the solution of direct and inverse surgical problems (i.e. soft tissue prediction, inverse implant shape design) demonstrate that the developed approach provides a useful tool for accurate prediction and optimisation of aesthetic surgery outcome. [ABSTRACT FROM AUTHOR]

In cranio-maxillofacial surgery physicians are often faced with the reconstruction of massively destroyed or radically resected tissue structures caused by trauma or tumours. Also corrections of dislocated bone fragments up to the complete modeling of facial regions in cases of complex congenital malformations are common tasks of plastic and reconstructive surgeons. With regard to the individual anatomy and physiology, such procedures have to be planned and executed thoroughly in order to achieve the best functional as well as an optimal aesthetic rehabilitation. On this account a computer-assisted modeling, planning and simulation approach is presented that allows for preoperative assessment of different therapeutic strategies on the basis of three-dimensional patient models. Bone structures can be mobilized and relocated under consideration of anatomical and functional constraints. The resulting facial appearance is simulated via finite-element methods on the basis of a biomechanical tissue model, and visualized using high quality rendering techniques. Such an approach is not only important for preoperative mental preparation, but also for vivid patient information, documentation, quality assurance as well as for surgical education and training. [ABSTRACT FROM AUTHOR]

A clinical case in maxillofacial surgery is presented, where a congenital hypoplasia of the midface with apparent dental malocclusion was to be treated. A 3D osteotomy planning on a polygonal skull model has been performed for different types of osteotomies, followed by a maxillary advancement under consideration of the implications on the facial soft tissue deformation, using a volumetric finite element approach. The simulation results of isotropic, homogeneous as well as inhomogeneous tissue models using a linear elastic modeling approach are quantitatively evaluated with the help of postoperative CT data. [Copyright &y& Elsevier]

Patients with severe facial deformities present serious dysfunctionalities along with an unsatisfactory aesthetic facial appearance. Several methods have been proposed to specifically plan the interventions on the patient's needs, but none of these seem to achieve a sufficient level of accuracy in predicting the resulting facial appearance. In this context, a deep knowledge of what occurs in the face after bony movements in specific surgeries would give the possibility to develop more reliable systems. This study aims to propose a novel 3D approach for the evaluation of soft tissue zygomatic modifications after zygomatic osteotomy; geometrical descriptors usually involved in face analysis tasks, i.e., face recognition and facial expression recognition, are here applied to soft tissue malar region to detect changes in surface shape. As ground truth for zygomatic changes, a zygomatic openness angular measure is adopted. The results show a high sensibility of geometrical descriptors in detecting shape modification of the facial surface, outperforming the results obtained from the angular evaluation. [ABSTRACT FROM AUTHOR]

Three-dimensional surgical planning is used widely in orthognathic surgery. Although numerous computer programs exist, the accuracy of soft tissue prediction remains uncertain. The purpose of this study was to compare the prediction accuracy of Dolphin, ProPlan CMF, and a probabilistic finite element method (PFEM). Seven patients (mean age 18years; five female) who had undergone Le Fort I osteotomy with preoperative and 1-year postoperative cone beam computed tomography (CBCT) were included. The three programs were used for soft tissue prediction using planned and postoperative maxillary position, and these were compared to postoperative CBCT. Accurate predictions were obtained with each program, indicated by root mean square distances: RMS Dolphin =1.8±0.8mm, RMS ProPlan =1.2±0.4mm, and RMS PFEM =1.3±0.4mm. Dolphin utilizes a landmark-based algorithm allowing for patient-specific bone-to-soft tissue ratios, which works well for cephalometric radiographs but has limited three-dimensional accuracy, whilst ProPlan and PFEM provide better three-dimensional predictions with continuous displacements. Patient or population-specific material properties can be defined in PFEM, while no soft tissue parameters are adjustable in ProPlan. Important clinical considerations are the topological differences between predictions due to the three algorithms, the non-negligible influence of the mismatch between planned and postoperative maxillary position, and the learning curve associated with sophisticated programs like PFEM., (Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Introduction: Throughout the history of computing, shortening the gap between the physical and digital world behind the screen has always been strived for. Recent advances in three-dimensional (3D) virtual surgery programs have reduced this gap significantly. Although 3D assisted surgery is now widely available for orthognathic surgery, one might still argue whether a 3D virtual planning approach is a better alternative to a conventional two-dimensional (2D) planning technique. The purpose of this study was to compare the accuracy of a traditional 2D technique and a 3D computer-aided prediction method., Methods: A double blind randomised prospective study was performed to compare the prediction accuracy of a traditional 2D planning technique versus a 3D computer-aided planning approach. The accuracy of the hard and soft tissue profile predictions using both planning methods was investigated., Results: There was a statistically significant difference between 2D and 3D soft tissue planning (p < 0.05). The statistically significant difference found between 2D and 3D planning and the actual soft tissue outcome was not confirmed by a statistically significant difference between methods., Conclusions: The 3D planning approach provides more accurate soft tissue planning. However, the 2D orthognathic planning is comparable to 3D planning when it comes to hard tissue planning. This study provides relevant results for choosing between 3D and 2D planning in clinical practice., (Copyright © 2015 European Association for Cranio-Maxillo-Facial Surgery. Published by Elsevier Ltd. All rights reserved.)