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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.

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 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.

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.

Purpose Facial soft tissue prediction in orthognathic surgery could be a valuable aid to preview the results and determine the best surgical treatment. After many years, considerable difficulties are still present in the prediction of the clinical final aspect. The object of the present study was to validate new soft tissue simulation software (SurgiCase CMF; Materialise, Leuven, Belgium), using data acquired by cone beam computed tomography (CBCT), that makes it possible to foresee the final result. Materials and Methods Ten patients with craniomaxillofacial deformations underwent CBCT before surgery. Using the SurgiCase CMF software, the data were reconstructed in 3 dimensions, and various osteotomies were simulated in a 3-dimensional virtual environment by applying different surgical procedures. At 6 months after surgery, the patients underwent repeat CBCT. Thus, it was possible to superimpose the pre- and postoperative CBCT studies to evaluate the reproducibility and reliability of the software. Results CBCT simulations defined an average absolute error of 0.94 mm, a standard deviation of 0.90 mm, and a percentage of error less than 2 mm of 86.80%. Conclusion The preliminary results have allowed us to conclude that simulations in orthognathic surgery for skull-maxillofacial deformities using CBCT acquisition are reliable, in addition to the low radiation exposure, and could become the reference standard to plan surgical treatment.