Evaluating soft tissue simulation in maxillofacial surgery using preoperative and postoperative CT scans

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 maxillary positioning using computer-designed and manufactured occlusal splints or patient-specific implants in orthognathic surgery

OBJECTIVE

To determine the accuracy of maxillary positioning using computer-designed and manufactured occlusal splints or patient-specific implants in orthognathic surgery.

MATERIAL AND METHODS

A retrospective analysis of 28 patients that underwent virtually planned orthognathic surgery with maxillary Le Fort I osteotomy either using VSP-generated splints (n = 13) or patient-specific implants (PSI) (n = 15) was conducted. The accuracy and surgical outcome of both techniques were compared by superimposing preoperative surgical planning with postoperative CT scans and measurement of translational and rotational deviation for each patient.

RESULTS

The 3D global geometric deviation between the planned position and the postoperative outcome was 0.60 mm (95%-CI 0.46-0.74, range 0.32-1.11 mm) for patients with PSI and 0.86 mm (95%-CI 0.44-1.28, range 0.09-2.60 mm) for patients with surgical splints. Postoperative differences for absolute and signed single linear deviations between planned and postoperative position were a little higher regarding the x-axis and pitch but lower regarding the y- and z-axis as well as yaw and roll for PSI compared to surgical splints. There were no significant differences regarding global geometric deviation, absolute and signed linear deviations in the x-, y-, and z-axis, and rotations (yaw, pitch, and roll) between both groups.

CONCLUSIONS

Regarding accuracy for positioning of maxillary segments after Le Fort I osteotomy in orthognathic surgery patient-specific implants and surgical splints provide equivalent high accuracy.

CLINICAL RELEVANCE

Patient-specific implants for maxillary positioning and fixation facilitate the concept of splintless orthognathic surgery and can be reliably used in clinical routines.

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 in Orthognathic Cases Using Dolphin Three-Dimensional Software

INTRODUCTION

Orthodontists and surgeons have been looking for more accurate methods to plan and predict surgical outcomes in patients with skeletal discrepancies.

METHODS

The sample consisted of 20 subjects from the surgical clinic of a graduate orthodontic program who had been treated with Le Fort I maxillary movement, bisagittal split osteotomy, with or without genioplasty. All subjects had to have preoperative (T0) and at least 6 months postoperative (T1) cone-beam computed tomographies that were imported to Dolphin three-dimensional (3D) software version 11.9 in digital imaging and communications in medicine format. Three-dimensional voxel-based superimposition on the cranial base was performed for T0 and T1 to accurately measure the skeletal surgical movements. A virtual orthognathic surgery was performed on T0 to mimic the actual skeletal osteotomies using the treatment simulation tool in Dolphin 3D. A prediction 3D soft tissue image (Tp) was generated based on the Dolphin virtual skeletal planning. The differences between Tp and T1 for all patients were measured using linear and angular measurements visualized by surface mapping.

RESULTS

Significant differences were found between Tp and T1 in Nasolabial angle, Soft tissue A point, and Subalar area.

CONCLUSIONS

The soft tissue prediction accuracy after double jaw surgery using Dolphin 3D is limited in some areas, especially upper lip and base of the nose.

The effect of bone displacement operations on facial soft tissues

A novel biomechanical model for face soft tissue (skin, mucosa, and muscles) is introduced to investigate the effect of mandible and chin bone displacement on the overall appearance of the patient's face. Nonlinear FE analysis is applied to the model and the results obtained are used to help surgeons to decide the amount of displacement required.

Finite element modelling of maxillofacial surgery and facial expressions--a preliminary study

BACKGROUND

Recent advances in diagnostic imaging and associated software have enabled the transformation of anatomical structures into finite element (FE) models facilitating computerized facial modelling. The work presented employs personalized imaging data of facial anatomical structures for use in planning and predicting the outcome of maxillofacial surgery. The current process relies on either freehand planning and/or commercial two-dimensional (2D) and three-dimensional (3D) surgical planning software packages, but the validity of these software packages has been questioned. In this paper, the finite element technique was used to predict the outcome of maxillofacial surgery.

METHODS

A finite element facial model was created, based on patient specific bone and skin image data, combined with generic muscle data. The model was used for two distinct purposes: simulation of the maxillofacial surgery and simulation of facial expressions pre- and post-surgery. This combination allowed for an improved prediction of surgery outcome. Commercial software was used for creating the FE facial model from the original image data, and LS-DYNA(®) was the analysis code used for the purpose of the FE simulation.

RESULTS

The results from the simulation of maxillofacial surgery showed an overall agreement of 85% with the patient data at 6 months post-surgery and the error was generally contained within a ± 2 mm threshold.

CONCLUSIONS

The results show that the approach of using commercial FE software tools to create a detailed and anatomically accurate patient specific model for simulation of maxillofacial surgery, as well as facial expressions, can be applied in facial surgery planning. As the study was based on data collected from one patient, further work is needed in which additional subjects can be assessed.