Accuracy of three-dimensional soft tissue simulation in bimaxillary osteotomies

Virtual surgical planning in orthognathic surgery: a dental hospital's 10-year experience

PURPOSE

The primary objective of this study was to evaluate how the utilization of virtual surgical planning (VSP) and the epidemiological profile of patients undergoing orthognathic surgery (OGS) have changed in the past decade.

METHODS

The records of patients who had undergone orthognathic surgery at a national dental hospital were reviewed. Trends in VSP, epidemiological data, presentation of dentofacial deformity, and management details were recorded.

RESULTS

A total of 1184 patients were included in this study. The majority of the patients seeking treatment in this dental hospital were young Chinese adults with dentofacial deformities requiring bimaxillary surgeries. Most patients presented with a skeletal Class III pattern (79.0%), and asymmetry was diagnosed in 80.8% of all cases.

CONCLUSION

There was an initial slow pick-up rate for VSP, but this rapidly increased to a high adoption rate of 98.7-100% between 2019 and 2021. Together with an increasing body of evidence suggesting greater accuracy in VSP, utilization in this technology can be enhanced with greater familiarity with the technology and improvements in the VSP services.

The Accuracy of Three-Dimensional Soft Tissue Simulation in Orthognathic Surgery-A Systematic Review

Three-dimensional soft tissue simulation has become a popular tool in the process of virtual orthognathic surgery planning and patient-surgeon communication. To apply 3D soft tissue simulation software in routine clinical practice, both qualitative and quantitative validation of its accuracy are required. The objective of this study was to systematically review the literature on the accuracy of 3D soft tissue simulation in orthognathic surgery. The Web of Science, PubMed, Cochrane, and Embase databases were consulted for the literature search. The systematic review (SR) was conducted according to the PRISMA statement, and 40 articles fulfilled the inclusion and exclusion criteria. The Quadas-2 tool was used for the risk of bias assessment for selected studies. A mean error varying from 0.27 mm to 2.9 mm for 3D soft tissue simulations for the whole face was reported. In the studies evaluating 3D soft tissue simulation accuracy after a Le Fort I osteotomy only, the upper lip and paranasal regions were reported to have the largest error, while after an isolated bilateral sagittal split osteotomy, the largest error was reported for the lower lip and chin regions. In the studies evaluating simulation after bimaxillary osteotomy with or without genioplasty, the highest inaccuracy was reported at the level of the lips, predominantly the lower lip, chin, and, sometimes, the paranasal regions. Due to the variability in the study designs and analysis methods, a direct comparison was not possible. Therefore, based on the results of this SR, guidelines to systematize the workflow for evaluating the accuracy of 3D soft tissue simulations in orthognathic surgery in future studies are proposed.

Accuracy of hard and soft tissue prediction using three-dimensional simulation software in bimaxillary osteotomies: A systematic review

INTRODUCTION

Orthognathic surgery is considered nowadays as a revolutionary treatment option for treating skeletal discrepancies and severe malocclusions in the sagittal, vertical and transverse dimensions. This surgery allows both the restoration of facial harmony and the achievement of satisfactory dental occlusion. The technology of computer-assisted surgeries including virtual surgical simulation programs and planning software greatly contributes to providing a three-dimensional simulation and precise mobilization of the maxilla and/or mandible, thus allowing the prediction of the final outcome in soft tissues. This study aims to systematically review the available scientific literature about the accuracy of the hard and soft tissue predictions delivered by the many promoted three-dimensional simulation software.

MATERIAL AND METHODS

An electronic search was conducted on various databases: Medline via PubMed, The Cochrane Library, EBSCO-host, and Web of Science. The search was established on a well-defined research question following PICO principle: population, intervention, comparator and outcome. Search evaluation and the assessment of risk of bias were undertaken in each study following its type and design.

RESULTS

Fifteen studies were included for qualitative analysis. Seven studies evaluated the accuracy of soft tissue prediction, seven focused more on the accuracy of hard tissue and one study assessed both hard and soft tissue prediction accuracy delivered by the simulation software. Moreover, three studies were judged to be low risk and four were classified as high risk. Included studies revealed that hard tissue prediction is highly accurate and reliable, leading to clinically acceptable results. Yet, soft tissue prediction is unclear due to various factors that bias its results. Caution should therefore be taken when providing information about the soft tissue planning to patients.

CONCLUSIONS

Computer assisted 3D simulation protocols allow for more precise repositioning of the maxilla and/or mandible compared to conventional 2D methods. However, 3D soft tissue prediction using simulation software remains less accurate, especially in the labial region.

Deep learning-enabled 3D multimodal fusion of cone-beam CT and intraoral mesh scans for clinically applicable tooth-bone reconstruction

High-fidelity three-dimensional (3D) models of tooth-bone structures are valuable for virtual dental treatment planning; however, they require integrating data from cone-beam computed tomography (CBCT) and intraoral scans (IOS) using methods that are either error-prone or time-consuming. Hence, this study presents Deep Dental Multimodal Fusion (DDMF), an automatic multimodal framework that reconstructs 3D tooth-bone structures using CBCT and IOS. Specifically, the DDMF framework comprises CBCT and IOS segmentation modules as well as a multimodal reconstruction module with novel pixel representation learning architectures, prior knowledge-guided losses, and geometry-based 3D fusion techniques. Experiments on real-world large-scale datasets revealed that DDMF achieved superior segmentation performance on CBCT and IOS, achieving a 0.17 mm average symmetric surface distance (ASSD) for 3D fusion with a substantial processing time reduction. Additionally, clinical applicability studies have demonstrated DDMF's potential for accurately simulating tooth-bone structures throughout the orthodontic treatment process.

A digital workflow to predict facial aesthetics in patients with maxillofacial trauma with implant retained prostheses

PURPOSE

To introduce a digital workflow for the prediction of facial aesthetics, especially in patients with dentation deformity caused by maxillofacial trauma.

METHODS

Cone-beam computed tomography (CBCT) and three-dimensional facial scans of patients with radiographic prostheses were collected. The aforementioned data were uploaded to ProPlan CMF software and merged to generate a virtual patient with craniofacial hard tissue, realistic facial soft tissue, and remaining dentition. The radiographic prostheses were scanned to form a digital cast, which was fitted with its CBCT image to create the virtual prostheses. Postoperative facial soft tissue was simulated according to the movement of the virtual prostheses. An appropriate virtual diagnostic prosthesis plan was selected by the patient and dentist. Subsequently, prosthetically driven implant guide and restoration were designed and fabricated.

CONCLUSIONS

A virtual patient was successfully constructed. A 4-mm protrusion of the virtual prosthesis was chosen. Subsequently, implant surgery was performed, and dental prostheses were fabricated based on this location. The fusion of the postoperative facial scan and preoperative facial prediction was found to be coincident. This technique can effectively predict facial aesthetic features of patients with maxillofacial trauma, facilitate communication with patients, reduce chairside time, and guide the multidisciplinary design of implant placement and restoration fabrication.

Evaluation of soft tissue prediction accuracy for orthognathic surgery with skeletal class III malocclusion using maxillofacial regional aesthetic units

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.

Facial changes in patients with skeletal class III deformity after bimaxillary surgery: an evaluation based on three-dimensional photographs registered with computed tomography

The objective of this study was to evaluate facial soft and hard tissue changes, individually and relative to each other, in patients with skeletal class III deformity after bimaxillary surgery using three-dimensional (3D) photos obtained by white light scanning. Thirty patients with skeletal class III deformity who underwent bimaxillary surgery were selected. Each patient underwent white light scanning and spiral computed tomography (CT) within two weeks before (T0) and six months after surgery (T1). The 3D photos were registered with CT soft tissue models for T0 and T1, and the skeletal area unaffected by treatment (cranial base) was used to register T0 and T1. Then, the 3D colour-coded map was analysed to assess both skeletal and soft tissue changes between T0 and T1. Changes in the 3D coordinates of each anatomical landmark were analysed using the Student's t-test. Maxillary advancement by 2-3 mm and mandibular recession by 5-6 mm were observed; the mandible was shortened in the vertical direction. Compared with the preoperative values, the nasal columella was 0.51 mm shorter, the upper lip was 0.71 mm longer, the base of the alar cartilage was 1.38 mm wider, and the nasolabial angle became larger. The ratio of change in the position of soft tissue point Sn to hard tissue point A was 0.73:1, and that of soft tissue point Pg to hard tissue point Pog was 0.86:1. Images obtained by structured white light scanning registered with CT can be used as an alternative to study facial changes after orthognathic surgery.

A Quantitative and Qualitative Clinical Validation of Soft Tissue Simulation for Orthognathic Surgery Planning

The purpose of this study was to perform a quantitative and qualitative validation of a soft tissue simulation pipeline for orthognathic surgery planning, necessary for clinical use. Simulation results were retrospectively obtained in 10 patients who underwent orthognathic surgery. Quantitatively, error was measured at 9 anatomical landmarks for each patient and different types of comparative analysis were performed considering two mesh resolutions, clinically accepted error, simulation time and error measured by means of percentage of the whole surface. Qualitatively, evaluation and binary questions were asked to two surgeons, both before and after seeing the actual surgical outcome, and their answers were compared. Finally, the quantitative and qualitative results were compared to check if these two types of validation are correlated. The quantitative results were accurate, with greater errors corresponding to gonions and lower lip. Qualitatively, surgeons answered similarly mostly and their evaluations improved when seeing the actual outcome of the surgery. The quantitative validation was not correlated to the qualitative validation. In this study, quantitative and qualitative validations were performed and compared, and the need to carry out both types of analysis in validation studies of soft tissue simulation software for orthognathic surgery planning was proved.

Accuracy of Three-Dimensional Soft-Tissue Prediction Considering the Facial Aesthetic Units Using a Virtual Planning System in Orthognathic Surgery

Virtual surgical planning (VSP) is commonly used in orthognathic surgery. A precise soft-tissue predictability would be a helpful tool, for determining the correct displacement distances of the maxilla and mandible. This study aims to evaluate the soft-tissue predictability of the VSP software IPS CaseDesigner^® (KLS Martin Group, Tuttlingen, Germany). Twenty patients were treated with bimaxillary surgery and were included in the study. The soft-tissue simulation, done by the VSP was exported as STL files in the engineering software Geomagic Control X^TM (3D systems, RockHill, SC, USA). Four months after surgery, a 3D face scan of every patient was performed and compared to the preoperative simulation. The quality of the soft-tissue simulation was validated with the help of a distance map. This distance map was calculated using the inter-surface distance algorithm between the preoperative simulation of the soft-tissue and the actual scan of the postoperative soft-tissue surface. The prediction of the cranial parts of the face (upper cheek, nose, upper lip) was more precise than the prediction of the lower areas (lower cheek, lower lip, chin). The percentage of correctly predicted soft-tissue for the face in total reached values from 69.4% to 96.0%. The VSP system IPS CaseDesigner^® (KLS Martin Group; Tuttlingen, Germany) predicts the patient’s post-surgical soft-tissue accurately. Still, this simulation has to be seen as an approximation, especially for the lower part of the face, and continuous improvement of the underlying algorithm is needed for further development.

Deep Learning-Based Prediction of the 3D Postorthodontic Facial Changes

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.

Comparison of soft tissue simulations between two planning software programs for orthognathic surgery

The aim of this study was to compare the soft tissue predicative abilities of two established programs depending on the surgical technique and amount of displacement. On the basis of 50 computed tomography images, 11 orthognathic operations with differences in displacement distances and technique (maxillary advancement, MxA; maxillary impaction, MxI; mandibular setback, MnS; mandibular advancement, MnA bimaxillary displacement, MxA/MnS) as well as corresponding soft tissue predictions were simulated using the programs Dolphin (D) and ProPlan (PP). For all the soft tissue predictions by the two programs, eight linear and two angular measurements were performed and compared. The simulation of maxillary impaction showed a similar soft tissue behaviour between the two programs. However, differences or divergent behaviours were observed for other procedures. In the middle third of the face these significant differences concerned in particular the nasolabial angle (Ns-Sn-Ls)(5 mm-MA, D: 119.9 ± 8.6° vs. PP: 129.5 ± 8.4°; 7 mm-MnS: D: 128.5 ± 8.2° vs. PP: 129.6 ± 8.1°; 10 mm-MnA D: 126.0 ± 8.0° vs. PP: 124.9 ± 8.4°; 5 mm-MxA/4 mm-MnS, D: 120.2 ± 8.7° vs. PP: 129.9 ± 8.3°; all p < 0.001) and in the lower third the mentolabial angle (Pog´-B´-Li) (5 mm-MA, D: 133.2 ± 11.4° vs. PP: 126.8 ± 11.6°; 7 mm-MnS: D: 133.1 ± 11.3° vs. PP: 124.6 ± 11.9°; 10 mm-MnA D: 133.3 ± 11.5° vs. PP: 146.3 ± 11.1°; bignathic 5 mm-MxA/4 mm-MnS, D: 133.1 ± 11.4° vs. PP: 122.7 ± 11.9°; all p < 0.001) and the distance of the inferior lip to the aesthetic Line (E-Line-Li) (5 mm-MA, D: 3.7 ± 2.3 mm vs. PP: 2.8 ± 2.5 mm; 7 mm-MnS: D: 5.1 ± 3.0 mm vs. PP: 3.3 ± 2.3 mm; 10 mm-MnA D: 2.5 ± 1.6 mm vs. PP: 3.9 ± 2.8 mm; bignathic 5 mm-MxA/4 mm-MnS, D: 4.8 ± 3.0 mm vs. PP: 2.9 ± 2.0 mm; all p < 0.001). The soft tissue predictions by the tested programs differed in simulation outcome, which led to the different, even divergent, results. However, the significant differences are often below a clinically relevant level. Consequently, soft tissue prediction must be viewed critically, and its actual benefit must be clarified.

Accuracy of 3-dimensional soft tissue prediction for orthognathic surgery in a Chinese population

OBJECTIVES

This study aims to determine the validity of a 3D planning software in predicting the soft tissue outcome of Chinese patients undergoing orthognathic surgery for correction of Skeletal III dentofacial deformity.

METHODS

Pre- and post-operative 3D facial stereophotogrammetric scans and cone beam computed tomography were taken for 10 Chinese patients who had underwent orthognathic surgery. The pre-operative 3D facial scan was integrated with the pre-operative CBCT using the ProPlan CMF software. The simulated soft tissue 3D face was then compared with the actual 3D facial scan obtained at least 6 months postoperatively. Two outcome measures were computed as follows (i) mean absolute difference between meshes (ii) percentage of points where the distance between the two meshes is 2mm or less.

RESULTS

The mean absolute difference between the predicted and actual soft tissue surface meshes for the full face and the 6 anatomic regions ranged from 0.72mm to 1.42 mm. The mean absolute distance between the meshes for all the anatomic regions were within 2 mm (p<0.05). The percentage of mesh points with less than 2mm error ranged from 72.5% to 92.5%. The accuracy of soft tissue prediction, assessed using mean absolute distance for the full face, was significantly correlated to the amount of sagittal surgical movement (r=0.707, p=0.022). The lower lip was also found to be the least accurate.

CONCLUSIONS

Using ProPlan CMF, the accuracy of 3D soft tissue predictions for bimaxillary orthognathic surgery in Chinese Skeletal III patients were clinically satisfactory.

Facial Contour Refining after Surgery-First SSRO with Computer-Assisted Design in East Asians

BACKGROUND

Surgery-first sagittal split ramus osteotomies (SF-SSRO) are an effective treatment for patients with dental malocclusion. However, some patients with mandibular prognathism usually have facial deficiencies which cannot be corrected completely after orthognathic surgery. These are not accepted because the remaining facial contours are in disharmony.

METHODS

Twenty-five patients, who were unsatisfied with their appearances after SF-SSRO and orthodontics, were included. The preoperative CTs were used to investigate patients for facial deformity. To achieve a harmonious facial contour, mandible long-curve osteotomy/mandible U-shaped osteotomy, genioplasty or facial autologous fat grafting was selected depending on patients' contour deformities with the assistance of CAD.

RESULTS

Among the patients who underwent osteotomy, the gonial angle was improved from 111.16° ± 9 to 111.58° ± 9.06 after SF-SSRO because of distal fragment setback and rotation. After facial refine surgery, the gonial angle was significantly increased to 121.69°±2.41 (p < 0.05). And the mandibular width was decreased from 11.29 cm ± 0.44 to 10.45 cm ± 0.39 (p < 0.05) after mandibular outer plate griding. All patients were shown no signs of infection, massive bleeding, and osteonecrosis in the early stage. After follow-up time, they were all satisfied with their results and most of them recovered from lip numbness.

CONCLUSIONS

This study indicated the clinical feasibility of two-stage orthognathic and facial bone contouring surgery for the treatment of dentofacial deformities. Two-stage facial contouring surgery can provide esthetic improvement for more accuracy in refining the facial contour.

LEVEL OF EVIDENCE IV

This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

Accuracy of three-dimensional soft tissue profile prediction in orthognathic surgery

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.

Three-dimensional virtual planning in mandibular advancement surgery: Soft tissue prediction based on deep learning

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). CONCLUSION: 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.

Three-Dimensional Evaluation of Soft Tissue Malar Modifications after Zygomatic Valgization Osteotomy via Geometrical Descriptors

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.

Accuracy of Soft Tissue Prediction of 2 Virtual Planning Systems in Patients Undergoing Intraoral Quadrangular Le Fort II Osteotomy

Virtual surgical planning (VSP) is state of the art in routine clinical work. Visualization of soft tissue changes adds important information for surgical planning. The aim of this study was to evaluate accuracy of soft tissue prediction of 2 VSP systems in patients undergoing an intraoral quadrangular Le Fort II osteotomy.

Fusion of intra-oral scans in cone-beam computed tomography scans

PURPOSE

The purpose of this study was to evaluate the clinical accuracy of the fusion of intra-oral scans in cone-beam computed tomography (CBCT) scans using two commercially available software packages.

MATERIALS AND METHODS

Ten dry human skulls were subjected to structured light scanning, CBCT scanning, and intra-oral scanning. Two commercially available software packages were used to perform fusion of the intra-oral scans in the CBCT scan to create an accurate virtual head model: IPS CaseDesigner® and OrthoAnalyzer™. The structured light scanner was used as a gold standard and was superimposed on the virtual head models, created by IPS CaseDesigner® and OrthoAnalyzer™, using an Iterative Closest Point algorithm. Differences between the positions of the intra-oral scans obtained with the software packages were recorded and expressed in six degrees of freedom as well as the inter- and intra-observer intra-class correlation coefficient.

RESULTS

The tested software packages, IPS CaseDesigner® and OrthoAnalyzer™, showed a high level of accuracy compared to the gold standard. The accuracy was calculated for all six degrees of freedom. It was noticeable that the accuracy in the cranial/caudal direction was the lowest for IPS CaseDesigner® and OrthoAnalyzer™ in both the maxilla and mandible. The inter- and intra-observer intra-class correlation coefficient showed a high level of agreement between the observers.

CLINICAL RELEVANCE

IPS CaseDesigner® and OrthoAnalyzer™ are reliable software packages providing an accurate fusion of the intra-oral scan in the CBCT. Both software packages can be used as an accurate fusion tool of the intra-oral scan in the CBCT which provides an accurate basis for 3D virtual planning.

Accuracy of three-dimensional virtual simulation of the soft tissues of the face in OrtogOnBlender for correction of class II dentofacial deformities: an uncontrolled experimental case-series study

Purpose

To assess whether virtual simulations of the projection of the soft tissues of the face after class II bimaxillary orthognathic surgery, generated from 3D reconstruction of preoperative computed tomography (CT) scans, differed significantly from the actual soft tissue profile obtained in the late postoperative period (beyond 6 months). Secondarily, to validate the accuracy of a free, open-source software suite for virtual soft tissue planning in orthognathic surgery.

Methods

Helical CT scans were obtained pre- and postoperatively from 16 patients with Angle class II malocclusion who underwent bimaxillary orthognathic surgery. A comparative study between soft tissue meshes constructed for surgical simulation (M1) and the actual meshes obtained from postoperative scans (M2) was then performed. To establish the accuracy of 3D facial soft tissue simulation in a free and open-source software suite (OrtogOnBlender-OOB), 17 predetermined anatomic landmarks were measured in M1 and M2 scans after alignment of cranial structures.

Results

The mean error between preoperative simulations and actual postoperative findings was < 2 mm for all anthropometric landmarks. The overall average error for the facial soft tissues was 1.07 mm.

Conclusion

Comparison between preoperative simulation (M1) and actual postoperative findings (M2) showed clinically relevant ability of the method to reproduce actual surgical movement reliably (< 2-mm error). OOB is capable of accurate soft tissue planning for orthognathic surgery, but mesh deformation methods still require improvement.

Trial registration

RBR-88jff9. Retrospectively registered at Brazilian Registry of Clinical trials-ReBec (http://www.ensaiosclinicos.gov.br) May 06, 2020.

3D Soft-Tissue Prediction Methodologies for Orthognathic Surgery—A Literature Review

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.

Three-dimensional printing of facial contour based on preoperative computer simulation and its clinical application

Facial contouring is a complex procedure performed to alter tissue contents and restore facial appearance. However, it is difficult to measure the amount of the tissue volume that is needed. This study demonstrated the use of preoperative computer simulation (PCS) and 3-dimensional (3D) printing in contouring procedure to maximize outcomes.Three-dimensional surface imaging (3DSI) or computed tomography imaging (CTI) data were reconstructed into a 3D model by Mimics software. PCS was performed by simulating the changes in bone and soft tissue. The stimulating volume change was calculated by Boolean operations. Finally, the virtual model was exported into 3D printer to produce physical templates to guide surgical plan. PCS and actual postoperative results were compared using objective rating scales and by cephalometrical measurements.With the direct guidance of PCS and 3D templates, contouring procedure was performed accurately. Satisfactory facial contouring was achieved with less operative time. As the plastic surgery panel rated, 45.8% of the 3DSI results and 41.7% of the CTI results were identical with the actual outcome, and 0% of them was poor. There were no significant differences in patient satisfaction between the PCS of 3DSI and CTI.Preoperative computer simulation is an accurate method for designing contour adjustment plans, and can be an efficient and reliable predictor of outcomes with customized templates.

Volumetric comparison of maxillofacial soft tissue morphology: computed tomography in the supine position versus three-dimensional optical scanning in the sitting position

OBJECTIVE

Three-dimensional (3-D) surgical simulation has become popular, but the accuracy of such simulation is difficult to assess. Because maxillofacial soft tissue profiles vary with posture, we compared such profiles obtained in the supine and sitting positions.

STUDY DESIGN

In total, 28 patients with skeletal Class III jaw deformities underwent computed tomography in the supine position and 3-D optical scanning in the sitting position. The 2 sets of 3-D data were superimposed, and linear and volumetric differences were calculated. We evaluated the cheeks, the subauricular and infraorbital regions, the nose, the lips, and the chin. Statistical analyses were performed by using paired Student's t tests. Differences with P < .05 were considered significant.

RESULTS

Patients were divided into 3 groups based on body mass index. The facial profiles of the cheeks and subauricular areas differed significantly between the sitting and supine positions. The extent of variation increased with body mass index.

CONCLUSIONS

When a patient moves from the sitting position to the supine position, maxillofacial soft tissue migrates from the cheeks to the subauricular regions. Thus, simulations for surgery based on supine computed tomography alone do not accurately model the cheeks and subauricular areas.

Three-dimensional aesthetic assessment of class II patients before and after orthognathic surgery and its association with quantitative surgical changes

The aim of this study was to compare evaluations of the aesthetic outcome of class II orthognathic patients, as performed by observers with varying expertise using three-dimensional (3D) facial images, and to examine the relationship of aesthetic ratings in relation to quantitative surgical changes. Pre- and postoperative 3D facial images of 20 surgically treated class II patients (13 female, 7 male) were assessed for aesthetics by orthodontists, maxillofacial surgeons, and laypeople. Attractiveness ratings for the lips, chin, and overall facial aesthetics were evaluated on a 5-point Likert scale. Correlation between the aesthetic scores was obtained and quantitative surgical changes were examined. For all groups of observers, significant improvements in attractiveness scores were found, especially for the chin assessment. Orthodontists perceived the greatest improvement and laypeople the smallest. Overall, laypeople scored higher with less variability, but with lower intra- and inter-observer agreement. No significant correlation was found between the aesthetic improvement and soft tissue surgical changes. To avoid patient dissatisfaction, it is important to bear in mind that the demands and perception of aesthetic improvement after orthognathic surgery are higher for clinicians than for the general public.

The accuracy of three-dimensional prediction of soft tissue changes following the surgical correction of facial asymmetry: An innovative concept

The accuracy of three-dimensional (3D) predictions of soft tissue changes in the surgical correction of facial asymmetry was evaluated in this study. Preoperative (T1) and 6-12-month postoperative (T2) cone beam computed tomography scans of 13 patients were studied. All patients underwent surgical correction of facial asymmetry as part of a multidisciplinary treatment protocol. The magnitude of the surgical movement was measured; virtual surgery was performed on the preoperative scans using Maxilim software. The predicted soft tissue changes were compared to the actual postoperative appearance (T2). Mean (signed) distances and mean (absolute) distances between the predicted and actual 3D surface meshes for each region were calculated. The one-sample t-test was applied to test the alternative hypothesis that the mean absolute distances had a value of <2.0mm. A novel directional analysis was applied to analyse the accuracy of the prediction of soft tissue changes. The results showed that the distances between the predicted and actual postoperative soft tissue changes were less than 2.0mm in all regions. The predicted facial morphology was narrower than the actual surgical changes in the cheek regions. 3D soft tissue prediction using Maxilim software in patients undergoing the correction of facial asymmetry is clinically acceptable.

A systematic review of soft-to-hard tissue ratios in orthognathic surgery. Part IV: 3D analysis - Is there evidence?

OBJECTIVE

The objective of this study was to determine the soft-to-hard tissue ratio using 3D analysis in different types of orthognathic surgery.

MATERIAL AND METHODS

A systematic search for relevant studies published in different languages through December 2016 was performed in the Medline, Embase, Lilacs, Scopus, and Science Direct databases. Additional studies were identified by reference analysis. The methodological quality of the included studies was evaluated using the method proposed by the Effective Public Health Practice Project. The level of evidence was assessed using the Level of Evidence (LOE) scale from the Oxford Center for Evidence-Based Medicine.

RESULTS

A total of 11 articles with 461 patients were included in the review, comprising 5 retrospective study and 6 prospective studies. The latter included 5 studies that performed anatomical area analyses of postoperative soft tissue results in the context of software-based planned surgeries without regard to hard tissues and 6 articles that presented analyses related to soft tissue in the preoperative and postoperative stage with certain points to be considered in hard tissues. The analysis of bias in the articles revealed a weak methodology in all included studies, which made it impossible to perform any type of comparison.

CONCLUSION

Bias governs many of the published studies related to 3D analysis. There are no data that can be applied to determine the soft-to-hard tissue ratio using 3D analysis in orthognathic surgery.

Combined soft and skeletal tissue modelling of normal and dysmorphic midface postnatal development

BACKGROUND

Midface hypoplasia as exemplified by Treacher Collins Syndrome (TCS) can impair appearance and function. Reconstruction involves multiple invasive surgeries with variable long-term outcomes. This study aims to describe normal and dysmorphic midface postnatal development through combined modelling of skeletal and soft tissues and to develop a surgical evaluation tool.

MATERIALS AND METHODS

Midface skeletal and soft tissue surfaces were extracted from computed tomography scans of 52 control and 14 TCS children, then analysed using dense surface modelling. The model was used to describe midface growth, morphology, and asymmetry, then evaluate postoperative outcomes.

RESULTS

Parameters responsible for the greatest variation in midface size and shape showed differences between TCS and controls with close alignment between skeletal and soft tissue models. TCS children exhibited midface dysmorphology and hypoplasia when compared with controls. Asymmetry was also significantly higher in TCS midfaces. Combined modelling was used to evaluate the impact of surgery in one TCS individual who showed normalisation immediately after surgery but reversion towards TCS dysmorphology after 1 year.

CONCLUSION

This is the first quantitative analysis of postnatal midface development using combined modelling of skeletal and soft tissues. We also provide an approach for evaluation of surgical outcomes, laying the foundations for future development of a preoperative planning tool.

Accuracy of three-dimensional facial soft tissue simulation in post-traumatic zygoma reconstruction

The aim of this study was to evaluate the accuracy of novel software-CMF-preCADS-for the prediction of soft tissue changes following repositioning surgery for zygomatic fractures. Twenty patients who had sustained an isolated zygomatic fracture accompanied by facial deformity and who were treated with repositioning surgery participated in this study. Cone beam computed tomography (CBCT) scans and three-dimensional (3D) stereophotographs were acquired preoperatively and postoperatively. The 3D skeletal model from the preoperative CBCT data was matched with the postoperative one, and the fractured zygomatic fragments were segmented and aligned to the postoperative position for prediction. Then, the predicted model was matched with the postoperative 3D stereophotograph for quantification of the simulation error. The mean absolute error in the zygomatic soft tissue region between the predicted model and the real one was 1.42±1.56mm for all cases. The accuracy of the prediction (mean absolute error ≤2mm) was 87%. In the subjective assessment it was found that the majority of evaluators considered the predicted model and the postoperative model to be 'very similar'. CMF-preCADS software can provide a realistic, accurate prediction of the facial soft tissue appearance after repositioning surgery for zygomatic fractures. The reliability of this software for other types of repositioning surgery for maxillofacial fractures should be validated in the future.

Three-dimensional virtual simulation of alar width changes following bimaxillary osteotomies

The aim of this study was to evaluate the accuracy of three-dimensional (3D) soft tissue simulation of nose width changes following bimaxillary osteotomies and to identify patient- and surgery-related factors that may affect the accuracy of simulation. Sixty patients (mean age 26 years) who underwent bimaxillary osteotomies participated in this study. Cone beam computed tomography scans were acquired preoperatively and at 1-year postoperative. The 3D hard and soft tissue rendered preoperative and postoperative virtual head models were superimposed, after which the maxilla and mandible were segmented and aligned to the postoperative position. The postoperative changes in alar width were simulated using a mass tensor model (MTM)-based algorithm and compared with the postoperative outcome. 3D cephalometric analyses were used to quantify the simulation error. The postoperative alar width was increased by 1.6±1.1mm and the mean error between the 3D simulation and the actual postoperative alar width was 1.0±0.9mm. The predictability was not correlated to factors such as age, sex, alar cinch suture, VY closure, maxillary advancement, or a history of surgically assisted rapid maxillary expansion. The MTM-based simulation model of postoperative alar width change was found to be reasonably accurate, although there is room for further improvement.

A novel method for the management of proximal segment using computer assisted simulation surgery: correct condyle head positioning and better proximal segment placement

Computer Assisted Simulation Surgery (CASS) is a reliable method that permits oral and maxillofacial surgeons to visualize the position of the maxilla and the mandible as observed in the patient. The purpose of this report was to introduce a newly developed strategy for proximal segment management according to Balanced Orthognathic Surgery (BOS) protocol which is a type of CASS, and to establish the clinical feasibility of the BOS protocol in the treatment of complex maxillo-facial deformities. The BOS protocol consists of the following 4 phases: 1) Planning and simulation phase, 2) Modeling phase, 3) Surgical phase, and 4) Evaluation phase. The surgical interventions in 80 consecutive patients were planned and executed by the BOS protocol. The BOS protocol ensures accuracy during surgery, thereby facilitating the completion of procedures without any complications. The BOS protocol may be a complete solution that enables an orthognatic surgeon to perform accurate surgery based on a surgical plan, making real outcomes as close to pre-planned outcomes as possible.