3D Mechanical Modeling of Facial Soft Tissue for Surgery Simulation

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.

The Art of Teaching, Training, and Putting the Scalpel in Residents' Hands

Surgical education is under tremendous pressure due to ever-increasing medical knowledge and demands on trainees' time. They must continually learn more in less time due to work hour limitations, regulations, and electronic medical record demands. Surgical training must become more efficient. There is an unprecedented array of education and training opportunities for resident preparation. The preparation for each case has to be maximal. Preoperative, intraoperative, and postoperative simulation and discussions improve the educational benefit of the trainee experience. For the teaching surgeon, putting a scalpel in residents' hands requires patience, knowledge, judgment, and a leap of faith in the resident.

Computational technology for nasal cartilage-related clinical research and application

Surgeons need to understand the effects of the nasal cartilage on facial morphology, the function of both soft tissues and hard tissues and nasal function when performing nasal surgery. In nasal cartilage-related surgery, the main goals for clinical research should include clarification of surgical goals, rationalization of surgical methods, precision and personalization of surgical design and preparation and improved convenience of doctor-patient communication. Computational technology has become an effective way to achieve these goals. Advances in three-dimensional (3D) imaging technology will promote nasal cartilage-related applications, including research on computational modelling technology, computational simulation technology, virtual surgery planning and 3D printing technology. These technologies are destined to revolutionize nasal surgery further. In this review, we summarize the advantages, latest findings and application progress of various computational technologies used in clinical nasal cartilage-related work and research. The application prospects of each technique are also discussed.

A novel soft tissue prediction methodology for orthognathic surgery based on probabilistic finite element modelling

Repositioning of the maxilla in orthognathic surgery is carried out for functional and aesthetic purposes. Pre-surgical planning tools can predict 3D facial appearance by computing the response of the soft tissue to the changes to the underlying skeleton. The clinical use of commercial prediction software remains controversial, likely due to the deterministic nature of these computational predictions. A novel probabilistic finite element model (FEM) for the prediction of postoperative facial soft tissues is proposed in this paper. A probabilistic FEM was developed and validated on a cohort of eight patients who underwent maxillary repositioning and had pre- and postoperative cone beam computed tomography (CBCT) scans taken. Firstly, a variables correlation assessed various modelling parameters. Secondly, a design of experiments (DOE) provided a range of potential outcomes based on uniformly distributed input parameters, followed by an optimisation. Lastly, the second DOE iteration provided optimised predictions with a probability range. A range of 3D predictions was obtained using the probabilistic FEM and validated using reconstructed soft tissue surfaces from the postoperative CBCT data. The predictions in the nose and upper lip areas accurately include the true postoperative position, whereas the prediction under-estimates the position of the cheeks and lower lip. A probabilistic FEM has been developed and validated for the prediction of the facial appearance following orthognathic surgery. This method shows how inaccuracies in the modelling and uncertainties in executing surgical planning influence the soft tissue prediction and it provides a range of predictions including a minimum and maximum, which may be helpful for patients in understanding the impact of surgery on the face.