Soft Tissue Prediction in Orthognathic Surgery: Improving Accuracy by Means of Anatomical Details

Overview

Journal PLoS One

Specialties General Medicine
Science

Date 2023 Nov 27

PMID 38011187

Authors

Federica Ruggiero

Alessandro Borghi

Mirko Bevini

Giovanni Badiali

Ottavia Lunari

David Dunaway

Claudio Marchetti

Affiliations

Soon will be listed here.

Abstract

Three-dimensional virtual simulation of orthognathic surgery is now a well-established method in maxillo-facial surgery. The commercial software packages are still burdened by a consistent imprecision on soft tissue predictions. In this study, the authors produced an anatomically detailed patient specific numerical model for simulation of soft tissue changes in orthognathic surgery. Eight patients were prospectively enrolled. Each patient underwent CBCT and planar x-rays prior to surgery and in addition received an MRI scan. Postoperative soft-tissue change was simulated using Finite Element Modeling (FEM) relying on a patient-specific 3D models generated combining data from preoperative CBCT (hard tissue) scans and MRI scans (muscles and skin). An initial simulation was performed assuming that all the muscles and the other soft tissue had the same material properties (Homogeneous Model). This model was compared with the postoperative CBCT 3D simulation for validation purpose. Design of experiments (DoE) was used to assess the effect of the presence of the muscles considered and of their variation in stiffness. The effect of single muscles was evaluated in specific areas of the midface. The quantitative distance error between the homogeneous model and actual patient surfaces for the midface area was 0.55 mm, standard deviation 2.9 mm. In our experience, including muscles in the numerical simulation of orthognathic surgery, brought an improvement in the quality of the simulation obtained.

Citing Articles

Anatomic and functional masseter muscle adaptation following orthognathic surgery-MRI analysis in 3 years of follow-up.

Duarte F, Silva J, Ramos C, Hopper C Maxillofac Plast Reconstr Surg. 2024; 46(1):26.

PMID: 39026066 PMC: 11258114. DOI: 10.1186/s40902-024-00437-6.

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

Olejnik A, Verstraete L, Croonenborghs T, Politis C, Swennen G J Imaging. 2024; 10(5).

PMID: 38786573 PMC: 11122049. DOI: 10.3390/jimaging10050119.

References

Kretschmer W, Zoder W, Baciut G, Bacuit M, Wangerin K . Accuracy of maxillary positioning in bimaxillary surgery. Br J Oral Maxillofac Surg. 2009; 47(6):446-9. DOI: 10.1016/j.bjoms.2009.06.004. View

Xia J, Gateno J, Teichgraeber J, Christensen A, Lasky R, Lemoine J . Accuracy of the computer-aided surgical simulation (CASS) system in the treatment of patients with complex craniomaxillofacial deformity: A pilot study. J Oral Maxillofac Surg. 2007; 65(2):248-54. DOI: 10.1016/j.joms.2006.10.005. View

Zhang X, Tang Z, Liebschner M, Kim D, Shen S, Chang C . An eFace-Template Method for Efficiently Generating Patient-Specific Anatomically-Detailed Facial Soft Tissue FE Models for Craniomaxillofacial Surgery Simulation. Ann Biomed Eng. 2015; 44(5):1656-71. PMC: 4833683. DOI: 10.1007/s10439-015-1480-7. View

Barbarino G, Jabareen M, Mazza E . Experimental and numerical study on the mechanical behavior of the superficial layers of the face. Skin Res Technol. 2011; 17(4):434-44. DOI: 10.1111/j.1600-0846.2011.00515.x. View

Xia J, Gateno J, Teichgraeber J, Yuan P, Chen K, Li J . Algorithm for planning a double-jaw orthognathic surgery using a computer-aided surgical simulation (CASS) protocol. Part 1: planning sequence. Int J Oral Maxillofac Surg. 2015; 44(12):1431-40. PMC: 4653096. DOI: 10.1016/j.ijom.2015.06.006. View

Mazzoni S, Bianchi A, Schiariti G, Badiali G, Marchetti C . Computer-aided design and computer-aided manufacturing cutting guides and customized titanium plates are useful in upper maxilla waferless repositioning. J Oral Maxillofac Surg. 2015; 73(4):701-7. DOI: 10.1016/j.joms.2014.10.028. View

Mundluru T, Almukhtar A, Ju X, Ayoub A . The accuracy of three-dimensional prediction of soft tissue changes following the surgical correction of facial asymmetry: An innovative concept. Int J Oral Maxillofac Surg. 2017; 46(11):1517-1524. DOI: 10.1016/j.ijom.2017.04.017. View

Kale E, Mumcuoglu E, Hamcan S . Automatic segmentation of human facial tissue by MRI-CT fusion: a feasibility study. Comput Methods Programs Biomed. 2012; 108(3):1106-20. DOI: 10.1016/j.cmpb.2012.07.006. View

Kim D, Kuang T, Rodrigues Y, Gateno J, Shen S, Wang X . A New Approach of Predicting Facial Changes following Orthognathic Surgery using Realistic Lip Sliding Effect. Med Image Comput Comput Assist Interv. 2019; 11768:336-344. PMC: 6934101. DOI: 10.1007/978-3-030-32254-0_38. View

10.

Ruggiero F, Badiali G, Bevini M, Marchetti C, Ong J, Bolognesi F . Parametrizing the genioplasty: a biomechanical virtual study on soft tissue behavior. Int J Comput Assist Radiol Surg. 2021; 17(1):55-64. PMC: 8739543. DOI: 10.1007/s11548-021-02489-9. View

11.

Knoops P, Borghi A, Ruggiero F, Badiali G, Bianchi A, Marchetti C . A novel soft tissue prediction methodology for orthognathic surgery based on probabilistic finite element modelling. PLoS One. 2018; 13(5):e0197209. PMC: 5942840. DOI: 10.1371/journal.pone.0197209. View

12.

Aboul-Hosn Centenero S, Hernandez-Alfaro F . 3D planning in orthognathic surgery: CAD/CAM surgical splints and prediction of the soft and hard tissues results - our experience in 16 cases. J Craniomaxillofac Surg. 2011; 40(2):162-8. DOI: 10.1016/j.jcms.2011.03.014. View

13.

Knoops P, Borghi A, Breakey R, Ong J, Jeelani N, Bruun R . Three-dimensional soft tissue prediction in orthognathic surgery: a clinical comparison of Dolphin, ProPlan CMF, and probabilistic finite element modelling. Int J Oral Maxillofac Surg. 2018; 48(4):511-518. DOI: 10.1016/j.ijom.2018.10.008. View

14.

Ter Horst R, van Weert H, Loonen T, Berge S, Vinayahalingam S, Baan F . Three-dimensional virtual planning in mandibular advancement surgery: Soft tissue prediction based on deep learning. J Craniomaxillofac Surg. 2021; 49(9):775-782. DOI: 10.1016/j.jcms.2021.04.001. View

15.

Tanikawa C, Yamashiro T . Development of novel artificial intelligence systems to predict facial morphology after orthognathic surgery and orthodontic treatment in Japanese patients. Sci Rep. 2021; 11(1):15853. PMC: 8339122. DOI: 10.1038/s41598-021-95002-w. View

16.

McCormick S, Drew S . Virtual model surgery for efficient planning and surgical performance. J Oral Maxillofac Surg. 2011; 69(3):638-44. DOI: 10.1016/j.joms.2010.10.047. View

17.

Knoops P, Beaumont C, Borghi A, Rodriguez-Florez N, Breakey R, Rodgers W . Comparison of three-dimensional scanner systems for craniomaxillofacial imaging. J Plast Reconstr Aesthet Surg. 2017; 70(4):441-449. DOI: 10.1016/j.bjps.2016.12.015. View

18.

Arda K, Ciledag N, Aktas E, Aribas B, Kose K . Quantitative assessment of normal soft-tissue elasticity using shear-wave ultrasound elastography. AJR Am J Roentgenol. 2011; 197(3):532-6. DOI: 10.2214/AJR.10.5449. View

19.

Peterman R, Jiang S, Johe R, Mukherjee P . Accuracy of Dolphin visual treatment objective (VTO) prediction software on class III patients treated with maxillary advancement and mandibular setback. Prog Orthod. 2016; 17(1):19. PMC: 4911347. DOI: 10.1186/s40510-016-0132-2. View

20.

Cevidanes L, Tucker S, Styner M, Kim H, Chapuis J, Reyes M . Three-dimensional surgical simulation. Am J Orthod Dentofacial Orthop. 2010; 138(3):361-71. PMC: 2994415. DOI: 10.1016/j.ajodo.2009.08.026. View