Accuracy of patient-specific soft-tissue prediction algorithms for maxillomandibular surgery in class III patients - Mehta, Shivam; Han, Michael; Tsay, T; Kusnoto, Budi (Contemporary clinical dentistry, 2022)
Context: Improved esthetics is an important factor for most patients undergoing orthognathic surgery. Thus, a treatment simulation that can provide patients with a realistic view of the esthetic outcome after surgery is important in clinical practice. Aims: To evaluate the accuracy of simulations generated using algorithms specific for patient's type of malocclusion and surgical procedure compared to nonspecific algorithms. Settings and Design: A total of 36 patients (average age 18.41 years) who had undergone maxillary advancement and mandibular setback for Class III malocclusion were included. Subjects and Methods: The presurgical and postsurgical cone-beam computed tomography scans were used to generate the lateral cephalograms and the surgical simulations were created with the patient-specific algorithm (specific for Class III patients) and the nonspecific algorithm (default algorithm not specific for any particular malocclusion or type of surgery) using the treatment simulation feature in Dolphin Imaging software. The accuracy of the simulations was examined by comparing the soft-tissue changes in the surgical simulations with the postsurgical result. Statistical Analysis Used: Statistical analyses were performed with SPSS-software at 0.05 significance level. For the mean difference between the postsurgical and surgical-simulation landmarks, a paired sample t-test (Student's t-test) was performed. Results: Patient-specific algorithms were accurate in vertical prediction of lower lip, B', tip of nose, upper lip, and horizontal prediction of pogonion'. Whereas the nonspecific algorithm was accurate in the horizontal prediction of the lower lip, pogonion', and menton'. Conclusions: Patient-specific and nonspecific algorithms for generating surgical simulations showed different accuracy for vertical and horizontal predictions of the parameters.