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This study aimed to evaluate the accuracy of the Digital Imaging software in the prediction of soft tissue changes following three types of orthodontic interventions: non-extraction, extraction, and orthognathic surgery treatments. Ninety-six patients were randomly selected from the records of three orthodontic interventions (32 subjects per group): (1) non-extraction, (2) extraction, and (3) orthodontic treatment combined with orthognathic surgery. The cephalometric analysis of soft tissue changes in both the actual post-treatment and the predicted treatment was performed using Dolphin Imaging software version 11.9. A paired t-test was utilized to assess the statistically significant differences between the predicted and actual treatment outcomes of the parameters (p < 0.05). In the non-extraction group, prediction errors were exhibited only in the lower lip parameters. In the extraction group, prediction errors were observed in both the upper and lower lip parameters. In the orthognathic surgery group, prediction errors were identified in chin thickness, facial contour angle, and upper and lower lip parameters (p < 0.05). Digital Imaging software exhibited inaccurate soft tissue prediction of 0.3–1.0 mm in some parameters of all treatment groups, which should be considered regarding the application of Dolphin Imaging software in orthodontic treatment planning.

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.

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.

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.